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Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications

Paper · 2017 · Farah Benamara, Maite Taboada and Yannick Mathieu

Summary

This survey connects linguistic theory with computational sentiment analysis. Most earlier reviews treated these fields separately. The authors argue that systems relying only on bags of words, or only on local sentence context, miss much of how evaluation works. They first review linguistic accounts of evaluative language, including stance, evidentiality, nonveridicality and especially the Appraisal framework. They then outline the standard computational formulation of the task. Their main contribution is a dynamic definition of evaluative expressions. In it, update functions adjust an expression's sentiment according to linguistic and extra-linguistic context, and these functions can operate at any discourse level. Using this framing, the paper covers lexical semantics, negation and modality, discourse structure and coherence relations, implicit and figurative evaluation, and social or user-profile signals. It presents no single experimental result. Instead, its central claim is that combining linguistic and discourse knowledge with statistical learning beats either approach alone. This argument sets an agenda for discourse-aware sentiment analysis and intent detection.

Key points

  • The paper describes three standard granularities in sentiment analysis: document, sentence, and aspect. It proposes a dynamic, context-sensitive definition based on update functions to go beyond them.
  • It notes the field's scale: about 31,000 Google Scholar hits for "sentiment analysis," and over 4,700 citations for the Pang and Lee survey alone.
  • Existing discourse parsers are trained on Wall Street Journal (Penn) data with RST, SDRT, or Penn Discourse TreeBank annotations, and they perform poorly on reviews. The SFU Review Corpus and CASOAR Corpus contain gold discourse and sentiment labels but are too small for training.
  • The authors suggest two ways to make discourse parsers more reliable for sentiment work. One is to drop relations that do not affect sentiment, such as framing and temporal ones. The other is to group the remaining relations, for example into support, opposition, causal, and structural types. They also propose merging the taxonomies of different theories, citing the COST Action TextLink.
  • Future directions include argument mining and intent detection, meaning the link from sentiment to action, such as predicting votes, deception, or purchase intent.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🔬Centre National de la Recherche ScientifiqueParis · France168 works · 62,369 citations🎓Simon Fraser UniversityBurnaby · Canada17 works · 4,413 citations🔬Institut de Recherche en Informatique de ToulouseToulouse · France35 works · 1,872 citations🎓Université de ToulouseToulouse · France16 works · 1,999 citations🔬Laboratoire de Linguistique FormelleParis · France13 works · 588 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksGeneral Social Sciences · 227 works

🧭 Topics 1Research subjects and languages the work is about.Sentiment & emotion · 972 works

Cite

Cite as Farah Benamara, Maite Taboada and Yannick Mathieu. 2017. Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications. Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00278

@article{benamara2017evaluative,
  title = {{Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications}},
  author = {Farah Benamara, Maite Taboada and Yannick Mathieu},
  journal = {Computational Linguistics},
  year = {2017},
  doi = {10.1162/coli\_a\_00278},
  url = {https://doi.org/10.1162/coli_a_00278}
}

References

373 references, 70 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Abbasi, Ahmed, Hsinchun Chen, and Arab Salem. 2008. Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums. ACM Transactions on Information Systems, 26(3):1–34. Afantenos, Stergos, Nicholas Asher, Farah Benamara, Myriam Bras, Cécile Fabre, Mai Ho-Dac, A… find
  2. Agarwal, Shashank and Hong Yu. 2009. Automatically classifying sentences in full-text biomedical articles into introduction, methods, results and discussion. Bioinformatics, 25(23):3174–3180. find
  3. Agarwal, Apoorv, Fadi Biadsy, and Kathleen R. McKeown. 2009. Contextual phrase-level polarity analysis using lexical affect scoring and syntactic n-grams. In Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009, pages 24–32, Athens. find
  4. Agarwal, Apoorv, Boyi Xie, Ilia Vovsha, Owen Rambow, and Rebecca Passonneau. 2011. Sentiment analysis of Twitter data. In Proceedings of the Workshop on Languages in Social Media, pages 30–38, Portland, OR.
  5. Agerri, Rodrigo and Ana Garcı́a-Serrano. 2010. Q-WordNet: Extracting polarity from WordNet senses. In Proceedings of the International Conference on Language Resources and Evaluation, pages 2300–2305, Malta. find
  6. Akkaya, Cem, Janyce Wiebe, and Rada Mihalcea. 2009. Subjectivity word sense disambiguation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 190–199, Singapore. find
  7. Akkaya, Cem, Janyce Wiebe, Alexander Conrad, and Rada Mihalcea. 2011. Improving the impact of subjectivity word sense disambiguation on contextual opinion analysis. In Proceedings of the Fifteenth Conference on Computational Natural Language Learning, pages 87–96, Portland, OR. find
  8. Andreevskaia, Alina and Sabine Bergler. 2008. When specialists and generalists work together: Overcoming domain dependence in sentiment tagging. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 290–298, Columbus, OH. find
  9. Argamon, Shlomo, Kenneth Bloom, Andrea Esuli, and Fabrizio Sebastiani. 2009. Automatically determining attitude type and force for sentiment analysis. In Zygmunt Vetulani and Hans Uszkoreit, editors, Human Language Technology: Challengies of the Information Society. Springer, Berlin, pages 218–231. find
  10. Arora, Deepali and Piyush Malik. 2015. Analitics: Key to go from generating big data to deriving business value. In Proceedings of the IEEE First International Conference on Big Data Computing Service and Applications, pages 446–452, San Francisco, CA. find
  11. Ashar, Jayen and Nitin Indukhya. 2010. A unifying view of computational discourse and natural language generation. ACM Computing Surveys, pages 1–30. find
  12. Asher, Nicholas and Alex Lascarides. 2003. Logics of Conversation. find
  13. Cambridge University Press. Asher, Nicholas, Farah Benamara, and Yvette Yannick Mathieu. 2008. Distilling opinion in discourse: A preliminary study. In Proceedings of the Computational Linguistics Conference, pages 7–10, Manchester. find
  14. Asher, Nicholas, Farah Benamara, and Yannick Mathieu. 2009. Appraisal of opinion expressions in discourse. Linguisticae Investigationes, 32(2):279–292. find
  15. Attardo, Salvatore. 2000. Irony as relevant inappropriateness. Journal of Pragmatics, 32(6):793–826. find
  16. Aue, Anthony and Michael Gamon. 2005. Customizing sentiment classifiers to new domains: A case study. In Proceedings of Recent Advances in Natural Language Processing, Borovets. find
  17. Baccianella, Stefano, Andrea Esuli, and Fabrizio Sebastiani. 2010. SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the Conference on International Language Resources and Evaluation, pages 2200–2204, Malta.
  18. Baker, Collin F., Charles J. Fillmore, and John B. Lowe. 1998. The Berkeley FrameNet Project. In Proceedings of the International Conference on Computational Linguistics - Volume 1, pages 86–90, Montréal.
  19. Bakhtin, Mikhail. 1981. Discourse in the novel. In Michael Holquist, editor, The Dialogic Imagination: Four Essays by M. M. Bakhtin. University of Texas Press, Austin, pages 259–422. Balahur, Alexandra and José M. Perea-Ortega. 2015. Sentiment analysis system adaptation for multilingual processing.… find
  20. Bamman, David and Noah A. Smith. 2015. Contextualized sarcasm detection on Twitter. In Proceedings of the International Conference on Web and Social Media, pages 574–577, Oxford. find
  21. Banfield, Ann. 1982. Unspeakable Sentences: Narration and Representation in the Language of Fiction. find
  22. Routledge and Kegan Paul, Boston. Barbe, Katharina. 1995. Irony in Context. find
  23. John Benjamins, Amsterdam. Barbieri, Francesco and Horacio Saggion. 2014. Modelling irony in Twitter: Feature analysis and evaluation. In Proceedings of Language Resources and Evaluation Conference, pages 4258–4264, Reykjavik.
  24. Becker, Israela and Vered Aharonson. 2010. Last but definitely not least: On the role of the last sentence in automatic polarity-classification. In Proceedings of the Annual Association of Computational linguistics (Volume 2), pages 331–335, Upsala. find
  25. Bednarek, Monika. 2006. Evaluation in Media Discourse: Analysis of a Newspaper Corpus. find
  26. Continuum, London. Benamara, Farah and Maite Taboada. 2015. Mapping different rhetorical relation annotations: A proposal. In Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, pages 147–152, Denver, CO. find
  27. Benamara, Farah, Carmine Cesarano, Antonio Picariello, Diego Reforgiato, and V. S. Subrahmanian. 2007. Sentiment analysis: Adjectives and adverbs are better than adjectives alone. In Proceedings of the International Conference on Weblogs and Social Media, Boulder, CO. find
  28. Benamara, Farah, Baptiste Chardon, Yannick Mathieu, and Vladimir Popescu. 2011. Towards context-based subjectivity analysis. In Proceedings of the International Joint Conference on Natural Language Processing, pages 1180–1188, Chian Mai. find
  29. Benamara, Farah, Baptiste Chardon, Yvette Yannick Mathieu, Vladimir Popescu, and Nicholas Asher. 2012. How do negation and modality impact opinions? In Proceedings of the ACL-2012 Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, pages 10–18, Jeju Island. Benamara, Far… find
  30. Benamara, Farah, Nicholas Asher, Yannick Mathieu, Vladimir Popescu, and Baptiste Chardon. 2016. Evaluation in discourse: A corpus-based study. Dialogue and Discourse, 7(1):1–49. find
  31. Bethard, Steven, Hong Yu, Ashley Thornton, Vasileios Hatzivassiloglou, and Dan Jurafsky. 2004. Automatic extraction of opinion propositions and their holders. In AAAI Spring Symposium on Exploring Attitude and Affect in Text, pages 22–24, Palo Alto, CA. find
  32. Bex, Floris, John Lawrence, Mark Snaith, and Chris Reed. 2013. Implementing the argument Web. Communications of the ACM, 56(10):66–73. find
  33. Bhatia, Parminder, Yangfeng Ji, and Jacob Eisenstein. 2015. Better document-level sentiment analysis from RST discourse parsing. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 2212–2218, Lisbon.
  34. Biber, Douglas and Edward Finegan. 1988. Adverbial stance types in English. Discourse Processes, 11(1):1–34. find
  35. Biber, Douglas and Edward Finegan. 1989. Styles of stance in English: Lexical and grammatical marking of evidentiality and affect. Text, 9(1):93–124. find
  36. Bieler, Heike, Stefanie Dipper, and Manfred Stede. 2007. Identifying formal and functional zones in film reviews. In Proceedings of the 8th SIGDIAL Workshop, pages 75–78, Antwerp. find
  37. Blitzer, John, Mark Dredze, and Fernando Pereira. 2007. Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 440–447, Prague.
  38. Bloom, Kenneth and Shlomo Argamon. 2010. Automated learning of Appraisals expression patterns. In Stefanie Wulff, Stefan Th. Gries, and Mark Davies, editors, Corpus-linguistic Applications: Current Studies, New Directions. Rodopi, Amsterdam, pages 249–260. find
  39. Bloom, Kenneth, Navendu Garg, and Shlomo Argamon. 2007. Extracting Appraisal expressions. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics / Human Language Technologies, pages 308–315, Rochester, NY. find
  40. Bollegala, Danushka, David Weir, and John Carroll. 2011. Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 132–141, Portland, OR. find
  41. Bollegala, Danushka, David J. Weir, and John A. Carroll. 2013. Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Transaction on Knowledge Data Engeneering, 25(8): 1719–1731. find
  42. Boyd-Graber, Jordan and Philip Resnik. 2010. Holistic sentiment analysis across languages: Multilingual supervised Latent Dirichlet Allocation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 45–55, Cambridge, MA. find
  43. Boye, Kasper and Peter Harder. 2009. Linguistic categories and grammaticalization. Functions of Language, 16(1):9–43. find
  44. Bradley, Margaret M. and Peter J. Lang. 1999. Affective norms for English words (ANEW): Stimuli, instruction manual, and affective ratings. Technical Report C-1, Center for Research in Psychophysiology, University of Florida. find
  45. Brafman, Ronen I. and Carmel Domshlak. 2009. Preference handling: An introductory tutorial. AI Magazine, 30(1):58–86. find
  46. Bratman, Michael. 1990. Dretske’s desires. Philosophy and Phenomenological Research, 50:795–800. find
  47. Brody, Samuel and Noemie Elhadad. 2010. An unsupervised aspect-sentiment model for online reviews. In Proceedings of the Annual Conference of the North American Association for Computational Linguistics, pages 804–812, Upsala.
  48. Brooke, Julian, Milan Tofiloski, and Maite Taboada. 2009. Cross-linguistic sentiment analysis: From English to Spanish. In Recent Advances in Natural Language Processing, pages 50–54, Borovets. find
  49. Bruce, Rebecca F. and Janyce M. Wiebe. 1999. Recognizing subjectivity: A case study in manual tagging. Natural Language Engineering, 5(2):187–205. Brun, Caroline and Caroline Hagège. 2013. Suggestion mining: Detecting suggestions for improvement in users’ comments. Research in Computing Science, 70… find
  50. Burfoot, Clint and Clint Baldwin. 2009. Automatic satire detection: Are you having a laugh? In Proceedings of the Annual Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing, pages 161–164, Singapore. find
  51. Burger, John D., John Henderson, George Kim, and Guido Zarrella. 2011. find
  52. Discriminating gender on Twitter. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, pages 1301–1309, Edinburgh.
  53. Buschmeier, Konstantin, Philipp Cimiano, and Roman Klinger. 2014. An impact analysis of features in a classification approach to irony detection in product reviews. In Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 42–49, Balti…
  54. Cambria, Erik and Amir Hussain. 2012. find
  55. Sentic Computing: Techniques, Tools, and Applications. Springer, Berlin. Cambria, Erik and Amir Hussain. 2015. Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis. find
  56. Springer, Berlin. Carlson, Lynn, Daniel Marcu, and Mary Ellen Okurowski. 2002. find
  57. RST Discourse Treebank. Linguistic Data Consortium. Philadelphia, PA. Carpuat, Marine. 2015. Connotation in translation. In Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 9–15, Lisbon. Carvalho, Paula, Luı́s Sarmento, Mário J.… find
  58. Chafe, Wallace and Johanna Nichols. 1986. Evidentiality: The Linguistic Coding of Epistemology. find
  59. Ablex, Norwood, NJ. Chafe, Wallace. 1986. Evidentiality in English conversation and academic writing. In Wallace Chafe and Johanna Nichols, editors, Evidentiality: The Linguistic Coding of Epistemology. Ablex, Norwood, NJ, pages 261–272. find
  60. Chafe, Wallace. 1994. Discourse Consciousness and Time: The Flow and Displacement of Conscious Experience in Speaking and Writing. University of Chicago Press, Chicago. find
  61. Charaudeau, Patrick. 1992. Grammaire du sens et de L’expression. find
  62. Hachette, Paris. Chardon, Baptiste, Farah Benamara, Yannick Mathieu, Vladimir Popescu, and Nicholas Asher. 2013a. Sentiment composition using a parabolic model. In Proceedings of the International Conference on Computational Semantics, pages 47–58, Potsdam. find
  63. Chardon, Baptiste, Farah Benamara, Yvette Yannick Mathieu, Vladimir Popescu, and Nicholas Asher. 2013b. Measuring the effect of discourse structure on sentiment analysis. In Proceedings of the Conference on Computational Linguistics and Intelligent Text Processing, pages 25–37, Samos. find
  64. Chen, Zheng, Fan Lin, Huan Liu, Yin Liu, Wei-Ying Ma, and Liu Wenyin. 2002. User intention modeling in web applications using data mining. World Wide Web, 5(3):181–191. Chen, Zhiyuan, Bing Liu, Meichun Hsu, Malú Castellanos, and Riddhiman Ghosh. 2013. Identifying intention posts in discussion forum… find
  65. Chenlo, Jose M., Alexander Hogenboom, and David E. Losada. 2013. Sentiment-based ranking of blog posts using Rhetorical Structure Theory. In Natural Language Processing and Information Systems, volume 7934 of Lecture Notes in Computer Science. Springer, Berlin, pages 13–24. find
  66. Choi, Yejin and Claire Cardie. 2008. Learning with compositional semantics as structural inference for subsentential sentiment analysis. In Proceedings of Empirical Methods in Natural Language Processing, pages 793–801, Waikiki, HI.
  67. Choi, Yejin, Eric Breck, and Claire Cardie. 2006. Joint extraction of entities and relations for opinion recognition. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 431–439, Sydney. find
  68. Clark, Herbert H. and Richard J. Gerrig. 1984. On the pretense theory of irony. Journal of Experimental Psychology: General, 113(1):121–126. find
  69. Clift, Rebecca. 1999. Irony in conversation. Language in Society, 28:523–553. find
  70. Clough, Patricia Ticineto and Jean O’Malley Halley. 2007. The Affective Turn: Theorizing the Social. find
  71. Duke University Press, Durham, NC. Cohen, Philip R. and Hector J. Levesque. 1990. Intention is choice with commitment. Artificial Intelligence, 42(2-3):213–261. find
  72. Conrad, Susan and Douglas Biber. 2000. Adverbial marking of stance in speech and writing. In Susan Hunston and Geoff Thompson, editors, Evaluation in Text: Authorial Distance and the Construction of Discourse. Oxford University Press, Oxford, pages 56–73. find
  73. Councill, Isaac G., Ryan McDonald, and Leonid Velikovich. 2010. What’s great and what’s not: Learning to classify the scope of negation for improved sentiment analysis. In Proceedings of the Workshop on Negation and Speculation in Natural Language Processing, pages 51–59, Uppsala. find
  74. Cruz, Noa, Maite Taboada, and Ruslan Mitkov. 2016. A machine learning approach to negation and speculation detection for sentiment analysis. Journal of the Association for Information Science and Technology, 67:2218–2136. Daille, Béatrice, Estelle Dubreil, Laura Monceaux, and Mathieu Vernier. 2011.… find
  75. Dave, Kushal, Steve Lawrence, and David M. Pennock. 2003. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the International Conference on World Wide Web, pages 519–528, Budapest. find
  76. Davidov, Dmitry, Oren Tsur, and Ari Rappoport. 2010. Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pages 107–116, Uppsala.
  77. Davidson, Richard J., Klaus R. Scherer, and H. Hill Goldsmith. 2003. Handbook of Affective Sciences. find
  78. Oxford University Press, Oxford. de Marneffe, Marie-Catherine, Christopher D. Manning, and Christopher Potts. 2012. Did it happen? The pragmatic complexity of veridicality assessment. Computational Linguistics, 38(2):301–333. de Melo, Gerard and Mohit Bansal. 2013. Good, great, excellent: Global inf…
  79. Deitrick, William and Wei Hu. 2013. Mutually enhancing community detection and sentiment analysis on Twitter networks. Journal of Data Analysis and Information Processing, 1(3):19–29. find
  80. Denecke, Kerstin. 2009. Are SentiWordNet scores suited for multi-domain sentiment classification? In IEEE International Conference on Digital Information Management, pages 33–38, Ann Arbor, MI. find
  81. Deng, Hongbo, Jiawei Han, Hao Li, Heng Ji, Hongning Wang, and Yue Lu. 2014a. Exploring and inferring user-user pseudo-friendship for sentiment analysis with heterogeneous networks. Statistical Analysis and Data Mining, 7(4):308–321. find
  82. Deng, Lingjia, Janyce Wiebe, and Yoonjung Choi. 2014b. Joint inference and disambiguation of implicit sentiments via implicature constraints. In Proceedings of the International Conference on Computational Linguistics, pages 79–88, Dublin.
  83. Denis, Alexandre, Samuel Cruz-Lara, Nadia Bellalem, and Lofti Bellalem. 2014. Synalp-Empathic: A valence shifting hybrid system for sentiment analysis. In Proceedings of the International Workshop on Semantic Evaluation, pages 605–609, Dublin. find
  84. Ding, Xiao, Ting Liu, Junwen Duan, and Jian-Yun Nie. 2015. Mining user consumption intention from social media using domain adaptive convolutional neural network. In Proceedings of the Conference on the American Association of Artificial Intelligence, pages 2389–2395. find
  85. Dong, Li, Furu Wei, Yajuan Duan, Xiaohua Liu, Ming Zhou, and Ke Xu. 2013. The automated acquisition of suggestions from tweets. In Proceedings of the Twenty-Seventh American Association for Artificial Intelligence, pages 239–245, Bellevue, WA. find
  86. Dong, Li, Furu Wei, Ming Zhou, and Ke Xu. 2014. Adaptive multi-compositionality for recursive neural models with applications to sentiment analysis. In Proceedings of the American Association for Artificial Intelligence, pages 1537–1543, Québec City. find
  87. Dowty, David R., Robert E. Wall, and Stanley Peters. 1981. Introduction to Montague Semantics. find
  88. Kluwer, Dordrecht. Dragut, Eduard, Hong Wang, Clement Yu, Prasad Sistla, and Weiyi Meng. 2012. Polarity consistency checking for sentiment dictionaries. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pages 997–1005, Jeju Island. find
  89. Ekman, Paul. 1984. Expression and the nature of emotion. In K. Scherer and P. Ekman, editors, Approaches to Emotion. Erlbaum, Hillsdale, NJ, pages 319–344. find
  90. Esuli, Andrea and Fabrizio Sebastiani. 2015. Optimizing text quantifiers for multivariate loss functions. ACM Transactions on Knowledge Discovery from Data, 10(1):Article 27. find
  91. Farzindar, Atefeh and Diana Inkpen. 2015. Natural Language Processing for Social Media. Synthesis Lectures on Human Language Technologies. Morgan & Claypool publishers. Fei, Geli, Bing Liu, Meichun Hsu, Malú Castellanos, and Riddhiman Ghosh. 2012. A dictionary-based approach to identifying aspects… find
  92. Feldman, Ronen. 2013. Techniques and applications for sentiment analysis: The main applications and challenges of one of the hottest research areas in computer science. Communications of the ACM, 56(4):82–89. Fellbaum, Christiane, editor. 1998. find
  93. WordNet: An Electronic Lexical Database. MIT Press, Cambridge, MA. Feng, Vanessa Wei and Graeme Hirst. 2014. A linear-time bottom–up discourse parser with constraints and post-editing. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 511–521, Baltimor…
  94. Feng, Song, Jun Seok Kang, Polina Kuznetsova, and Yejin Choi. 2013. Connotation lexicon: A dash of sentiment beneath the surface meaning. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 1774–1784, Sofia. find
  95. Feng, Vanessa Wei. 2015. RST-style discourse parsing and its applications in discourse analysis. Ph.D. thesis, Computer Science, University of Toronto, Canada. find
  96. Fisher, Seeger and Brian Roark. 2007. The utility of parse-derived features for automatic discourse segmentation. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, pages 488–495, Prague. find
  97. Fitzpatrick, Eileen, Joan Bachenko, and Tommaso Fornaciari. 2015. Automatic Detection of Verbal Deception. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers. Francisco, Virginia, Pablo Gervàs, and Federico Peinado. 2010. Ontological reasoning for improving the treatmen… find
  98. Fries, Peter H. 1995. Themes method of development and texts. In R. Hasan and P. Fries, editors, On Subject and Theme: A Discourse Functional Perspective. John Benjamins, Amsterdam/Philadelphia, pages 317–359. find
  99. Gamon, Michael. 2004. Sentiment classification on customer feedback data: Noisy data, large feature vectors, and the role of linguistic analysis. In Proceedings of the International Conference on Computational Linguistics, pages 841–847, Geneva. find
  100. Ganapathibhotla, Murthy and Bing Liu. 2008. Mining opinions in comparative sentences. In Proceedings of the 22nd International Conference on Computational Linguistics, pages 241–248, Manchester. find
  101. Gangemi, Aldo, Valentina Presutti, and Diego Reforgiato Recupero. 2014. Frame-based detection of opinion holders and topics: A model and a tool. IEEE Computational Intelligence Magazine, 9(1):20–30. Ganu, Gayatree, Yogesh Kakodkar, and Amélie Marian. 2013. Improving the quality of predictions using… find
  102. Gao, Dehong, Furu Wei, Wenjie Li, Xiaohua Liu, and Ming Zhou. 2015. Cross-lingual sentiment lexicon learning with bilingual word graph label propagation. Computational Linguistics, 41(1):21–40. find
  103. Georgeff, Michael P., Barney Pell, Martha E. Pollack, Milind Tambe, and Michael Wooldridge. 1999. The belief-desire-intention model of agency. In Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages, pages 1–10. find
  104. Gerani, Shima, Yashar Mehdad, Giuseppe Carenini, Raymond T. Ng, and Bita Nejat. 2014. Abstractive summarization of product reviews using discourse structure. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1602–1613.
  105. Ghosh, Aniruddha, Guofu Li, Tony Veale, Paolo Rosso, Ekaterina Shutova, John Barnden, and Antonio Reyes. 2015. SemEval-2015 task 11: Sentiment analysis of figurative language in Twitter. In Proceedings of the International Workshop on Semantic Evaluation, Co-located with NAACL, pages 470–478, Denver…
  106. Giannakidou, Anastasia. 1995. On the semantic licensing of polarity items. In Anastasios-Phoevos Christidis, Maria Margariti-Roga, and Argyris Arhakis, editors, Studies in Greek Linguistics 15: Proceedings of the Annual Meeting of the Department of Linguistics, pages 406–418, University of Thessalon… find
  107. Gianti, Andrea, Cristina Bosco, Viviana Patti, Andrea Bolioli, and Luigi Di Caro. 2012. Annotating irony in a novel Italian corpus for sentiment analysis. In Proceedings of the Workshop on Corpora for Research on Emotion Sentiment and Social Signals, pages 1–7, Istanbul. find
  108. Gibbs, Raymond W. 2000. Irony in talk among friends. Metaphor and Symbol, 15(1–2):5–27. find
  109. Ginsberg, Benjamin. 2011. The Fall of the Faculty: The Rise of the All-administrative University and Why it Matters. find
  110. Oxford University Press, Oxford. Goldberg, Andrew B. and Xiaojin Zhu. 2006. Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization. In Proceedings of the Workshop on Graph Based Methods for Natural Language Processing, pages 45–52, New York City,… find
  111. Goldberg, Andrew B., Nathanael Fillmore, David Andrzejewski, Zhiting Xu, Bryan Gibson, and Xiaojin Zhu. 2009. May all your wishes come true: A study of wishes and how to recognize them. In Proceedings of Human Language Technologies and the Annual Conference of the North American Chapter of the Assoc… find
  112. Goutsos, Dyonisos. 1996. A model of sequential relations in expository test. Text, 16(4):501–533. Goyal, Amit, Ellen Riloff, and Hal Daumé III. 2010. Automatically producing plot unit representations for narrative text. In Proceedings of the Conference on Empirical Methods in Natural Language Proce… find
  113. Grice, H. Paul. 1975. Logic and conversation. In Peter Cole and Jerry L. Morgan, editors, Speech Acts. Syntax and Semantics, Volume 3. Academic Press, New York, pages 41–58. find
  114. Grosz, Barbara J. and Candace L. Sidner. 1986. Attention, intentions, and the structure of discourse. Computational Linguistics, 12(3):175–204.
  115. Grosz, Barbara J., Aravind K. Joshi, and Scott Weinstein. 1995. Centering: A framework for modelling the local coherence of discourse. Computational Linguistics, 21(2): 203–225. find
  116. Haas, Michael and Yannick Versley. 2015. Subsentential sentiment on a shoestring: A crosslingual analysis of compositional classification. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 694–704, Denv… find
  117. Hai, Zhen, Kuiyu Chang, and Jung-jae Kim. 2011. Implicit feature identification via co-occurrence association rule mining. In Proceedings of the International Conference on Computational Linguistics and Intelligent Text Processing, pages 393–404, Tokyo. find
  118. Hai, Zhen, Kuiyu Chang, and Gao Cong. 2012. One seed to find them all: Mining opinion features via association. In Proceedings of the ACM International Conference on Information and Knowledge Management, CIKM, pages 255–264, Maui, HI. find
  119. Hall, David Leo Wright, Greg Durrett, and Dan Klein. 2014. Less grammar, more features. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 228–237, Baltimore, MD. find
  120. Halliday, Michael A. K. and Ruqaiya Hasan. 1976. Cohesion in English. find
  121. Longman, London. Halliday, Michael A. K. and Christian M. I. M. Matthiessen. 2014. An Introduction to Functional Grammar. Arnold, London, 4th edition. find
  122. Halliday, Michael A. K. 1985. An Introduction to Functional Grammar. Arnold, London, 1st edition. find
  123. Hardmeier, Christian. 2013. Discourse in statistical machine translation: A survey and a case study. Discours, 11. find
  124. Hasan, Kazi Saidul and Vincent Ng. 2013. Extra-linguistic constraints on stance recognition in ideological debates. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 816–821, Sofia. find
  125. Hasan, Kazi Saidul and Vincent Ng. 2014. Why are you taking this stance? Identifying and classifying reasons in ideological debates. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 751–762, Doha.
  126. Hatzivassiloglou, Vasileios and Kathleen R. McKeown. 1997. Predicting the semantic orientation of adjectives. In Proceedings of the Annual Meeting of the Association for Computational Linguistics and the Conference of the European Chapter of the Association for Computational Linguistics, pages 174–1… find
  127. Haviland, Susan E. and Herbert H. Clark. 1974. What’s new? Acquiring new information as a process in comprehension. Journal of Verbal Learning and Verbal Behaviour, 13:512–521. find
  128. He, Yulan and Deyu Zhou. 2011. Self-training from labeled features for sentiment analysis. Information Processing Management, 47(4):606–616. find
  129. He, Yulan, Chenghua Lin, Wei Gao, and Kam-Fai Wong. 2013. Dynamic joint sentiment-topic model. ACM Transactions on Intelligent Systems and Technology, 5(1):6. find
  130. Hearst, Marti A. 1992. Direction-based text interpretation as an information access refinement. In Paul S. Jacobs, editor, Text-based Intelligent Systems. Erlbaum, Hillsdale, NJ, pages 257–274. find
  131. Hearst, Marti A. 1994. Multi-paragraph segmentation of expository text. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 9–16, Las Cruces, NM. find
  132. Heerschop, Bas, Frank Goossen, Alexander Hogenboom, Flavius Frasincar, Uzay Kaymak, and Franciska de Jong. 2011. Polarity analysis of texts using discourse structure. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pages 1061–1070, Glasgow. Hernández…
  133. Hobbs, Jerry. 1979. Coherence and coreference. Cognitive Science (3), 8:67–90. find
  134. Hogenboom, Alexander, Frederik Hogenboom, Uzay Kaymak, Paul Wouters, and Franciska de Jong. 2010. Mining economic sentiment using argumentation structures. In Advances in Conceptual Modeling, Applications and Challenges, volume 6413 of Lecture Notes in Computer Science. Springer, Berlin, pages 200–2… find
  135. Hogenboom, Alexander, Flavius Frasincar, Franciska de Jong, and Uzay Kaymak. 2015. Using rhetorical structure in sentiment analysis. Communications of the ACM, 58(7):69–77. find
  136. Horn, Laurence. 1989. A Natural History of Negation. University of Chicago Press. find
  137. Hu, Minqing and Bing Liu. 2004a. Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 168–177, Seattle, WA. find
  138. Hu, Minqing and Bing Liu. 2004b. Mining opinion features in customer reviews. In Proceedings of the American Association for Artificial Intelligence, pages 755–760, San Jose, CA. find
  139. Hunston, Susan and Gill Francis. 2000. Pattern Grammar: A Corpus-Driven Approach to the Lexical Grammar of English. find
  140. John Benjamins, Amsterdam. Hunston, Susan and Geoff Thompson. 2000a. Evaluation: An introduction. In Susan Hunston and Geoff Thompson, editors, Evaluation in Text: Authorial Distance and the Construction of Discourse. Oxford University Press, Oxford, pages 1–27. Hunston, Susan and Geoff Thompson, ed… find
  141. Oxford University Press, Oxford. Hunston, Susan. 2011. Corpus Approaches to Evaluation: Phraseology and Evaluative Language. find
  142. Routledge, New York. Izard, Carroll Ellis. 1971. The Face of Emotion. find
  143. Appleton Century Crofts, New York. Jakob, Niklas and Iryna Gurevych. 2010. Extracting opinion targets in a single- and cross-domain setting with conditional random fields. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1035–1045, Cambridge, MA.
  144. Ji, Yangfeng and Jacob Eisenstein. 2014. Representation learning for text-level discourse parsing. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 13–24, Baltimore, MD.
  145. Jia, Lifeng, Clement Yu, and Weiyi Meng. 2009. The effect of negation on sentiment analysis and retrieval effectiveness. In Proceedings of the ACM Conference on Information and Knowledge Management, pages 1827–1830, Hong Kong. find
  146. Jiang, Jing and ChengXiang Zhai. 2007. Instance weighting for domain adaptation in NLP. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 264–271, Prague.
  147. Jindal, Nitin and Bing Liu. 2006a. Identifying comparative sentences in text documents. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development on Information Retrieval, pages 244–251, Seattle, WA. find
  148. Jindal, Nitin and Bing Liu. 2006b. Mining comparative sentences and relations. In Proceedings of the American Association for Artificial Intelligence, pages 1331–1336, Boston, MA. find
  149. Jing-Schmidt, Zhuo. 2007. Negativity bias in language: A cognitive-affective model of emotive intensifiers. Cognitive Linguistics, 18(3):417–443. find
  150. Johansson, Richard and Alessandro Moschitti. 2013. Relational features in fine-grained opinion analysis. Computational Linguistics, 39(3):473–509. Joshi, Mahesh and Carolyn Penstein-Rosé. 2009. Generalizing dependency features for opinion mining. In Proceedings of the Annual Meeting of the Associat…
  151. Joty, Shafiq, Giuseppe Carenini, and Raymond Ng. 2015. CODRA: A novel discriminative framework for rhetorical analysis. Computational Linguistics, 41(3):385–435.
  152. Kaci, Souhila. 2011. Working with Preferences: Less Is More. find
  153. Cognitive Technologies. Springer. Kamp, Hans and Uwe Reyle. 1993. From Discourse to Logic. find
  154. Kluwer, Dordrecht. Kang, Jun Seok, Song Feng, Leman Akoglu, and Yejin Choi. 2014. ConnotationWordNet: Learning connotation over the word+sense network. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 1544–1554, Baltimore, MD. Karoui, Jihen, Farah Ben… find
  155. Kennedy, Alistair and Diana Inkpen. 2006. Sentiment classification of movie and product reviews using contextual valence shifters. Computational Intelligence, 22(2):110–125. find
  156. Affective Computing and Sentiment Analysis: Emotion, Metaphor and Terminology. Springer, Berlin. Kim, Soo-Min and Eduard Hovy. 2004. Determining the sentiment of opinions. In Proceedings of the 20th International Conference on Computational Linguistics, pages 1367–1373, Geneva.
  157. Kim, Soo-Min and Eduard Hovy. 2005. Identifying opinion holders for question answering in opinion texts. In Proceedings of AAAI Workshop on Question Answering in Restricted Domains, Pittsburgh, PA. find
  158. Kim, Soo-Min and Eduard Hovy. 2006. Extracting opinions, opinion holders, and topics expressed in online news media text. In Proceedings of the ACL Workshop on Sentiment and Subjectivity in Text, pages 1–8, Sydney.
  159. Knott, Alistair and Robert Dale. 1994. Using linguistic phenomena to motivate a set of coherence relations. Discourse Processes, 18:35–62. find
  160. Koppel, Moshe and Jonathan Schler. 2006. The importance of neutral examples for learning sentiment. Computational Intelligence, 22(2):100–109. find
  161. Kreuz, Roger J. and Gina M. Caucci. 2007. Lexical influences on the perception of sarcasm. In Proceedings of the Workshop on Computational Approaches to Figurative Language, pages 1–4, Rochester, NY.
  162. Kunneman, Florian, Christine Liebrecht, Margot van Mulken, and Antal van den Bosch. 2015. Signaling sarcasm: From hyperbole to hashtag. Information Processing and Management, 51:500–509. find
  163. Lakoff, George and Mark Johnson. 1980. Metaphors We Live By. University of Chicago Press, Chicago. find
  164. Langacker, Ronald W. 1990. Subjectification. Cognitive Linguistics, 1(1):5–38. find
  165. Lawrence, John and Chris Reed. 2015. Combining argument mining techniques. In Working Notes of the 2nd Argumentation Mining Workshop, pages 127–136, Denver, CO. find
  166. Lazaridou, Angeliki, Ivan Titov, and Caroline Sporleder. 2013. A Bayesian model for joint unsupervised induction of sentiment, aspect and discourse representations. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 1630–1639, Sofia. find
  167. Lee, Christopher J. and Albert N. Katz. 1998. The differential role of ridicule in sarcasm and irony. Metaphor and Symbol, 13(1):1–15. find
  168. Leung, Cane Wing-Ki, Chi-Fai Chan Stephen, Fu-Lai Chung, and Grace Ngai. 2011. A probabilistic rating inference framework for mining user preferences from reviews. World Wide Web, 14(2):187–215. find
  169. Levelt, Willem J. M. 1981. The speaker’s linearization problem. Philosophical Transactions Royal Society London. Biological Sciences, 295:305–315. find
  170. Levin, Beth. 1993. English Verb Classes and Alternations: A Preliminary Investigation. University of Chicago Press, Chicago. find
  171. Li, Shoushan, Sophia Y. M. Lee, Ying Chen, Chu-Ren Huang, and Guodong Zhou. 2010. Sentiment classification and polarity shifting. In Proceedings of the 23rd International Conference on Computational Linguistics, pages 635–643, Beijing. find
  172. Li, Shoushan, Zhongqing Wang, Guodong Zhou, and Sophia Yat Mei Lee. 2011. Semi-supervised learning for imbalanced sentiment classification. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pages 1826–1831, Barcelona. find
  173. Li, Shasha, Chin-Yew Lin, Young-In Song, and Zhoujun Li. 2013a. Comparable entity mining from comparative questions. IEEE Transactions on Knowledge and Data Engineering, 25(7):1498–1509. find
  174. Li, Shoushan, Yunxia Xue, Zhongqing Wang, and Guodong Zhou. 2013b. Active learning for cross-domain sentiment classification. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pages 2127–2133, Beijing. find
  175. Li, Huayi, Arjun Mukherjee, Jianfeng Si, and Bing Liu. 2015. Extracting verb expressions implying negative opinions. In Proceedings of the Twenty-Ninth American Association on Artificial Intelligence, pages 2411–2417, Austin, TX. find
  176. Liang, Jiguang, Xiaofei Zhou, Li Guo, and Shuo Bai. 2015. Feature selection for sentiment classification using matrix factorization. In Proceedings of the International Conference on World Wide Web, pages 63–64, Florence. find
  177. Liebrecht, Christine, Florian Kunneman, and Antal van den Bosch. 2013. The perfect solution for detecting sarcasm in tweets \#not. In Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 29–37, Atlanta, GA.
  178. Lin, Chenghua and Yulan He. 2009. Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM Conference on Information and Knowledge Management, pages 375–384, Hong Kong. find
  179. Liu, Jingjing and Stephanie Seneff. 2009. Review sentiment scoring via a parse-and-paraphrase paradigm. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 161–169, Singapore. find
  180. Liu, Feifan, Wang Dong, Li Bin, and Liu Yang. 2010. Improving blog polarity classification via topic analysis and adaptive methods. In Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the ACL, pages 309–312, Los Angeles, CA. find
  181. Liu, Yang, Xiaohui Yu, Bing Liu, and Zhongshuai Chen. 2014. Sentence-level sentiment analysis in the presence of modalities. In Computational Linguistics and Intelligent Text Processing, volume 8404 of CICLing 2014, Kathmandu, pages 1–16. find
  182. Liu, Bing. 2012. Sentiment Analysis and Opinion Mining. find
  183. Morgan & Claypool. Liu, Bing. 2015. find
  184. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, Cambridge. Lizhen, Qu, Georgiana Ifrim, and Gerhard Weikum. 2010. The bag-of-opinions method for review rating prediction from sparse text patterns. In Proceedings of the International Conference on Computatio… find
  185. Lochbaum, Karen Elizabeth. 1998. Using collaborative plans to model the intentional structure of discourse. Computational Linguistics, 24:525–572. find
  186. Lu, Yue, ChengXiang Zhai, and Neel Sundaresan. 2009. Rated aspect summarization of short comments. In Proceedings of the 18th International Conference on World Wide Web, pages 131–140, Madrid. find
  187. Maks, Isa and Piek Vossen. 2012. A lexicon model for deep sentiment analysis and opinion mining applications. Decision Support Systems, 53(4):680–688. find
  188. Mann, William C. and Sandra A. Thompson. 1988. Rhetorical Structure Theory: Toward a functional theory of text organization. Text, 8(3):243–281.
  189. Manoussos, Katakis Ioannis, Varlamis Iraklis, and Tsatsaronis George. 2014. Pythia: Employing lexical and semantic features for sentiment analysis. In Machine Learning and Knowledge Discovery in Databases, volume 8726 of Lecture Notes in Computer Science, pages 448–451. find
  190. Springer, Berlin. Mao, Yi and Guy Lebanon. 2007. Isotonic conditional random fields and local sentiment flow. In Proceedings of the 21th Conference on Neural Information Processing Systems, pages 961–968, Vancouver. find
  191. Martin, James R. and Peter White. 2005. The Language of Evaluation: Appraisal in English. Palgrave, New York. Martin-Wanton, Tamara, Aurora Pons-Porrata, Andrés Montoyo, and Alexandra Balahur. 2010. Word sense disambiguation in opinion mining: Pros and cons. Journal on Research in Computing Science… find
  192. Martin, James R. 2000. Beyond exchange: Appraisal systems in English. In Susan Hunston and Geoff Thompson, editors, Evaluation in Text: Authorial Distance and the Construction of Discourse. Oxford University Press, Oxford, pages 142–175. find
  193. Martin, James R. 2014. Evolving Systemic Functional Linguistics: Beyond the clause. Functional Linguistics, 1(3):1–24. find
  194. Mathieu, Yvette Yannick and Christiane Fellbaum. 2010. Verbs of emotion in French and English. In The 5th International Conference of the Global WordNet Association, Mumbai. find
  195. Mathieu, Yvette Yannick. 2005. Annotation of emotions and feelings in texts. In Affective Computing and Intelligent Interaction, volume 3784 of Lecture Notes in Computer Science. Springer, Berlin Heidelberg, pages 350–357. find
  196. Matsumoto, Shotaro, Hiroya Takamura, and Manabu Okumura. 2005. Sentiment classification using word sub-sequences and dependency sub-trees. In Proceedings of the 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pages 301–311, Hanoi. find
  197. Maynard, Diana and Mark A. Greenwood. 2014. Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. In Proceedings of the Ninth International Conference on Language Resources and Evaluation, pages 4238–4243, Reykjavik.
  198. McDonald, Ryan, Kerry Hannan, Tyler Neylon, Mike Wells, and Jeff Reynar. 2007. Structured models for fine-to-coarse sentiment analysis. In Proceedings of the Association for Computational Linguistics, pages 432–439, Prague. find
  199. Mihalcea, Rada, Carmen Banea, and Janyce Wiebe. 2007. Learning multilingual subjective language via cross-lingual projections. In Proceedings of the Association for Computational Linguistics, pages 976–983, Prague, Czech Republic.
  200. Mitchell, Margaret, Jacqui Aguilar, Theresa Wilson, and Benjamin Van Durme. 2013. find
  201. Open domain targeted sentiment. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1643–1654, Seattle, WA. Mittal, Namita, Basant Agarwal, Garvit Chouhan, Prateek Pareek, and Nitin Bania. 2013. Discourse based sentiment analysis for Hindi reviews. In Pattern…
  202. Mochales, Raquel and Marie-Francine Moens. 2011. Argumentation mining. Artificial Intelligence and Law, 19(1):1–22. find
  203. Moghaddam, Samaneh and Martin Ester. 2011. ILDA: Interdependent LDA model for learning latent aspects and their ratings from online product reviews. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 665–674, Beijing Mohammad, Sa… find
  204. Mohammad, Saif M. 2016. Sentiment analysis: Detecting valence, emotions, and other affectual states from text. In Herb Meiselman, editor, Emotion Measurement, pages 201–237. find
  205. Elsevier, Amsterdam. Moilanen, Karo and Stephen Pulman. 2007. Sentiment composition. In Proceedings of Recent Advances in Natural Language Processing, pages 378–382, Borovets. find
  206. Moore, Johanna D. and Cecile L. Paris. 1993. Planning text for advisory dialogues: Capturing intentional and rhetorical information. Computational Linguistics, 19:651–694. find
  207. Morante, Roser and Caroline Sporleder. 2012a. Modality and negation: An introduction to the special issue. Computational Linguistics, 38(2):223–260.
  208. Morante, Roser and Caroline Sporleder. 2012b. Special issue on modality and negation. Computational Linguistics, 38(2):223–260. find
  209. Mukherjee, Subhabrata and Pushpak Bhattacharyya. 2012. Sentiment analysis in Twitter with lightweight discourse analysis. In Proceedings of International Conference on Computational Linguistics, pages 1847–1864, Mumbai. find
  210. Mullen, Tony and Collier Nigel. 2004. Sentiment analysis using support vector machines with diverse information sources. In Proceedings of Conference on Empirical Methods in Natural Language Processing, pages 412–418, Barcelona. find
  211. Muller, Philippe, Stergos Afantenos, Denis Pascal, and Nicholas Asher. 2012. Constrained decoding for text-level discourse parsing. In Proceedings of the Computational Linguistics Conference, pages 1883–1900, Mumbai.
  212. Musto, Cataldo, Giovanni Semeraro, and Marco Polignano. 2014. A comparison of lexicon-based approaches for sentiment analysis of microblog posts. In Proceedings of the 8th International Workshop on Information Filtering and Retrieval Co-located with XIII AI/*IA Symposium on Artificial Intelligence (… find
  213. Nakagawa, Tetsuji, Kentaro Tetsuji Inui, and Sadao Kurohashi. 2010. Dependency tree-based sentiment classification using CRFs with hidden variables. In Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 786–794, Lo…
  214. Narayanan, Ramanathan, Bing Liu, and Alok Choudhary. 2009. Sentiment analysis of conditional sentences. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 180–189, Singapore. find
  215. Naskar, Debashis, Sidahmed Mokaddem, Miguel Rebollo, and Eva Onaindia. 2016. Sentiment analysis in social networks through topic modeling. In Proceedings of the Tenth International Conference on Language Resources and Evaluation, pages 46–53, Portoroz. find
  216. Negi, Sapna and Paul Buitelaar. 2015. Towards the extraction of customer-to-customer suggestions from reviews. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 2159–2167, Lisbon. find
  217. Neviarouskaya, Alena and Masaki Aono. 2013. Sentiment word relations with affect, judgment and appreciation. IEEE Transactions on Affective Computing, 4(4):81–102. find
  218. Neviarouskaya, Alena, Helmut Prendinger, and Mitsuru Ishizuka. 2010a. @AM: Textual Attitude Analysis Model. In Proceedings of the 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pages 80–88, Los Angeles, CA. find
  219. Neviarouskaya, Alena, Helmut Prendinger, and Mitsuru Ishizuka. 2010b. Recognition of affect, judgment, and appreciation in text. In Proceedings of the 23rd International Conference on Computational Linguistics, pages 806–814, Beijing. find
  220. Neviarouskaya, Alena. 2010. Compositional Approach for Automatic Recognition of Fine-Grained Affect, Judgment, and Appreciation in Text. Ph.D. dissertation, University of Tokyo. find
  221. Ng, Vincentm, Sajib Dasgupta, and S. M. Niaz Arifin. 2006. Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews. In Proceedings of the Conference on Computational Linguistics and the Annual Meeting of the Association for Computational Lingu… find
  222. Nguyen, Thien Hai and Kiyoaki Shirai. 2015. Topic modeling based sentiment analysis on social media for stock market prediction. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pages… find
  223. Ortony, Andrew, Gerald L. Clore, and Allan Collins. 1988. The Cognitive Structure of Emotions. find
  224. Cambridge University Press, Cambridge. Osgood, Charles E. and Meredith Martin Richards. 1973. From yang and yin to and or but. Language, 49(2):380–412. find
  225. Osgood, Charles E., Georges J. Suci, and Percy H. Tannenbaum. 1957. The Measurement of Meaning. University of Illinois Press, Urbana. find
  226. Palau, Raquel Mochales and Marie-Francine Moens. 2009. Argumentation mining: The detection, classification and structure of arguments in text. In Proceedings of the 12th International Conference on Artificial Intelligence and Law, pages 98–107, Barcelona. find
  227. Paltoglou, Georgios and Mike Thelwall. 2013. More than bag-of-words: Sentence-based document representation for sentiment analysis. In Proceedings of Recent Advances in Natural Language Processing, RANLP 2013, pages 546–552, Hissar. find
  228. Pan, Sinno Jialin, Xiaochuan Ni, Jian-Tao Sun, Qiang Yang, and Zheng Chen. 2010. Cross-domain sentiment classification via spectral feature alignment. In Proceedings of the 19th International Conference on World Wide Web, pages 751–760, Raleigh, NC. find
  229. Pang, Bo and Lillian Lee. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, pages 271–278, Barcelona.
  230. Pang, Bo and Lillian Lee. 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 115–124, Barcelona.
  231. Pang, Bo and Lillian Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2):1–135. find
  232. Pang, Bo, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up?: Sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 79–86, Philadelphia, PA.
  233. Peldszus, Andreas and Manfred Stede. 2013. From argument diagrams to argumentation mining in texts: A survey. International Journal of Cognitive Informatics and Natural Intelligence, 7(1):1–31. find
  234. Peldszus, Andreas and Manfred Stede. 2015. Joint prediction in MST-style discourse parsing for argumentation mining. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, pages 938–948, Lisbon. Perret, Jérémy, Stergos Afantenos, Nicholas Asher, and…
  235. Persing, Isaac and Vincent Ng. 2014. Vote prediction on comments in social polls. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pages 1127–1138, Doha. find
  236. Perugini, Marco and Richard P. Bagozzi. 2004. The distinction between desires and intentions. European Journal of Social Psychology, 34(1):69–84. find
  237. Picard, Rosemary W. 1997. find
  238. Affective Computing. MIT Press, Cambridge, MA. Polanyi, Livia and Martin van den Berg. 2011. Discourse structure and sentiment. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops, pages 97–102, Vancouver. find
  239. Polanyi, Livia and Annie Zaenen. 2006. Contextual valence shifters. In James G. Shanahan, Yan Qu, and Janyce Wiebe, editors, Computing Attitude and Affect in Text: Theory and Applications. Springer, Berlin, pages 1–10. find
  240. Popat, Kashyap, Balamurali A.R, Pushpak Bhattacharyya, and Gholamreza Haffari. 2013. The haves and the have-nots: Leveraging unlabelled corpora for sentiment analysis. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 412–422, Sofia, Bulgaria. find
  241. Popescu, Ana-Maria and Oren Etzioni. 2005. Extracting product features and opinions from reviews. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pages 339–346, Vancouver.
  242. Poria, Soujanya, Alexander Gelbukh, Amir Hussain, Dipankar Das, and Sivaji Bandyopadhyay. 2013. Enhanced SenticNet with affective labels for concept-based opinion mining. IEEE Intelligent Systems, 28(2):31–38. find
  243. Potts, Christopher. 2010. On the negativity of negation. In Nan Li and David Lutz, editors, Proceedings of Semantics and Linguistic Theory, volume 20, pages 636–659, Ithaca, NY. find
  244. Prasad, Rashmi, Alan Lee, Nikhil Dinesh, Eleni Miltsakaki, Geraud Campion, Aravind K. Joshi, and Bonnie Webber. 2008. Penn Discourse Treebank version 2.0. In Proceedings of the Sixth International Conference on Language Resources and Evaluation, pages 2961–2968, Marrakech. find
  245. Purver, Matthew. 2011. Topic segmentation. In Gokhan Tur and Renato De Mori, editors, Spoken Language Understanding: Systems for Extracting Semantic Information from Speech. Wiley, Chichester, pages 291–317. find
  246. Qadir, Ashequl, Ellen Riloff, and Marilyn Walker. 2015. Learning to recognize affective polarity in similes. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 190–200, Lisbon. find
  247. Qiu, Guang, Bing Liu, Jiajun Bu, and Chun Chen. 2009. Expanding domain sentiment lexicon through double propagation. In Proceedings of the 21st International Joint Conference on Artifical Intelligence, pages 1199–1204, Pasadena, CA. find
  248. Qiu, Minghui, Yanchuan Sim, Noah A. Smith, and Jing Jiang. 2015. Modeling user arguments, interactions and attributes for stance prediction in online debate forums. In SIAM International Conference on Data Mining, pages 855–863, Vancouver. Rai, Piyush, Avishek Saha, Hal Daumé, III, and Suresh Venka… find
  249. In Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing, pages 27–32, Los Angeles, CA. find
  250. Raksha, Sharma, Agarwal Astha, Gupta Mohit, and Bhattacharyya Pushpak. 2015. Adjective intensity and sentiment analysis. In Conference on Empirical Methods in Natural Language Processing, pages 2520–2526, Lisbon. find
  251. Ramanand, J., Krishna Bhavsar, and Niranjan Pedanekar. 2010. Wishful thinking: Finding suggestions and ’buy’ wishes from product reviews. In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pages 54–61, Los Angeles, CA. find
  252. Rao, Delip, David Yarowsky, Abhishek Shreevats, and Manaswi Gupta. 2010. Classifying latent user attributes in Twitter. In Proceedings of the 2nd International Workshop on Search and Mining User-generated Contents, pages 37–44, Toronto. find
  253. Read, Jonathon and John Carroll. 2012a. Annotating expressions of Appraisal in English. Language Resources and Evaluation, 46:421–447. find
  254. Read, Jonathon and John Carroll. 2012b. Weakly supervised Appraisal analysis. Linguistic Issues in Language Technology, 8(2):1–21. find
  255. Rehbein, Ines, Merel Scholman, and Vera Demberg. 2015. Annotating discourse relations in spoken language: A comparison of the PDTB and CCR frameworks. In Proceedings of the Workshop on Identification and Annotation of Discourse Relations in Spoken Language, Saarbrücken.
  256. Ren, Yong, Nobuhiro Kaji, Naoki Yoshinaga, and Masaru Kitsuregawa. 2014. Sentiment classification in under-resourced languages using graph-based semi-supervised learning methods. IEICE Transactions on Information and Systems, 97-D(4):790–797. find
  257. Ren, Yafeng, Yue Zhang, Meishan Zhang, and Donghong Ji. 2016. Context-sensitive twitter sentiment classification using neural network. In Proceedings of the Thirtieth Conference on the American Association of Artificial Intelligence, pages 215–221, Phoenix, AZ. find
  258. Reyes, Antonio and Paolo Rosso. 2012. Making objective decisions from subjective data: Detecting irony in customer reviews. Decision Support Systems, 53(4):754–760. find
  259. Reyes, Antonio, Paolo Rosso, and Tony Veale. 2013. A multidimensional approach for detecting irony in Twitter. Language Resources and Evaluation, 47(1):239–268. Ribeiro, Filipe Nunes, Matheus Araújo, Pollyanna Gonçalves, Fabrı́cio Benevenuto, and Marcos André Gonçalves. 2016. A benchmark compari… — [arXiv:1512.01818](https://arxiv.org/abs/1512.01818) find
  260. Rick, Scott and George Loewenstein. 2008. The role of emotion in economic behavior. In Michael Lewis, Jeannette M. Haviland-Jones, and Lisa Feldman Barrett, editors, Handbook of Emotions. Guilford, New York, pages 138–156, 3rd edition. find
  261. Riloff, Ellen and Janyce Wiebe. 2003. Learning extraction patterns for subjective expressions. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 25–32, Sapporo.
  262. Riloff, Ellen, Ashequl Qadir, Prafulla Surve, Lalindra De Silva, Nathan Gilbert, and Ruihong Huang. 2013. Sarcasm as contrast between a positive sentiment and negative situation. In Conference on Empirical Methods in Natural Language Processing, pages 704–714, Seattle, WA. Roberto, John A., Maria Sa…
  263. Rozin, Paul and Edward B. Royzman. 2001. Negativity bias, negativity dominance, and contagion. Personality and Social Psychology Review, 5(4):296–320. find
  264. Ruppenhofer, Josef and Ines Rehbein. 2012. Semantic frames as an anchor representation for sentiment analysis. In Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, pages 104–109, Jeju Island. find
  265. Ruppenhofer, Josef, Swapna Somasundaran, and Janyce Wiebe. 2008. Finding the sources and targets of subjective expressions. In Proceedings of the Sixth International Language Resources and Evaluation, pages 2781–2788, Marrakech. find
  266. Ruppenhofer, Josef, Michael Wiegand, and Jasper Brandes. 2014. Comparing methods for deriving intensity scores for adjectives. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014, pages 117–122. find
  267. Russell, James A. 1983. Pancultural aspects of the human conceptual organization of emotions. Journal of Personality and Social Psychology, 45:1281–1288. find
  268. Samdani, Rajhans and Wen-Tau Yih. 2011. Domain adaptation with ensemble of feature groups. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI 2011, pages 1458–1464, Barcelona. Saurı́, Roser and James Pustejovsky. 2009. FactBank: A corpus annotated wi… find
  269. Scherer, Klaus R., Tanja Banziger, and Etienne Roesch. 2010. A Blueprint for Affective Computing. find
  270. Oxford University Press, Oxford. Shaikh, Mostafa Al, Helmut Prendinger, and Ishizuka Mitsuru. 2007. Assessing sentiment of text by semantic dependency and contextual valence analysis. In Proceedings of the 2nd International Conference on Affective Computing and Intelligent Interaction, ACII 2007, pa… find
  271. Sharma, Raksha, Mohit Gupta, Astha Agarwal, and Pushpak Bhattacharyya. 2015. Adjective intensity and sentiment analysis. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, pages 2520–2526, Lisbon. find
  272. Shutova, Ekaterina and Simone Teufel. 2010. Metaphor corpus annotated for source-target domain mappings. In Proceedings of the International Language Resources and Evaluation, LREC 2010, pages 3255–3261, Malta.
  273. Shutova, Ekaterina, Simone Teufel, and Anna Korhonen. 2013. Statistical metaphor processing. Computational Linguistics, 39(2):301–353.
  274. Shutova, Ekaterina. 2015. Design and evaluation of metaphor processing systems. Computational Linguistics, 41(4):579–623. find
  275. Smith, Phillip and Mark G. Lee. 2014. Acknowledging discourse function for sentiment analysis. In Computational Linguistics and Intelligent Text Processing, CICLing 2014, pages 45–52, Kathmandu. find
  276. Snyder, Benjamin and Regina Barzilay. 2007. Multiple aspect ranking using the good grief algorithm. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, pages 300–307, Rochester, NY. find
  277. Socher, Richard, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, and Christopher D. Manning. 2011. Semi-supervised recursive autoencoders for predicting sentiment distributions. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 151–161, Edinburgh, UK.
  278. Socher, Richard, Brody Huval, Christopher D. Manning, and Andrew Y. Ng. 2012. Semantic compositionality through recursive matrix-vector spaces. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 1201–1211, Jej…
  279. Socher, Richard, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1631–1…
  280. Somasundaran, Swapna and Janyce Wiebe. 2009. Recognizing stances in online debates. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing, pages 226–234, Singapore. find
  281. Somasundaran, Swapna, Galileo Namata, Janyce Wiebe, and Lise Getoor. 2009. Supervised and unsupervised methods in employing discourse relations for improving opinion polarity classification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 170–179, Singapor…
  282. Soricut, Radu and Daniel Marcu. 2003. Sentence level discourse parsing using syntactic and lexical information. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics in Human Language Technology, pages 149–156, Edmonton.
  283. Sperber, Dan and Deirdre Wilson. 1981. Irony and the use-mention distinction. Radical Pragmatics, 49:295–318. find
  284. Spertus, Ellen. 1997. Smokey: Automatic recognition of hostile messages. In Proceedings of the American Association of Artificial Intelligence and Ninth Conference on Innovative Applications of Artificial Intelligence, pages 1058–1065, Providence, RI. find
  285. Stab, Christian and Iryna Gurevych. 2014. Identifying argumentative discourse structures in persuasive essays. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 46–56, Doha.
  286. Stede, Manfred. 2011. Discourse Processing. Synthesis Lectures on Human Language Technologies. find
  287. Morgan & Claypool Publishers. Stone, Philip J., Bales Robert F., J. Zvi Namenwirth, and Daniel M. Ogilvie. 1962. The general inquirer: A computer system for content analysis and retrieval based on the sentence as a unit of information. Systems Research and Behavioral Science, 7(4):484–498. find
  288. Stoyanov, Veselin and Claire Cardie. 2008. Topic identification for fine-grained opinion analysis. In Proceedings of the International Conference on Computational Linguistics, pages 817–824, Manchester. find
  289. Strapparava, Carlo and Alessandro Valitutti. 2004. WordNet-Affect: An affective extension of WordNet. In Proceedings of the International Conference on Language Resources and Evaluation, pages 1083–1086, Lisbon.
  290. Su, Qi, Xinying Xu, Honglei Guo, Zhili Guo, Xian Wu, Xiaoxun Zhang, Bin Swen, and Zhong Su. 2008. Hidden sentiment association in Chinese Web opinion mining. In Proceedings of the 17th International Conference on World Wide Web, pages 959–968, Beijing. find
  291. Sujay, Cohan and Carlos M. Yalamanchi. 2012. Intention analysis for sales, marketing and customer service. In International Conference on Computational Linguistics, pages 33–40, Mumbai. find
  292. Sumath, Chiraag and Diana Inkpen. 2015. How much does word sense disambiguation help in sentiment analysis of micropost data? In Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 115–121, Lisbon. Surdeanu, Miahi, Thomas Hicks, and… find
  293. Swales, John M. 1990. Genre Analysis: English in Academic and Research Settings. Cambridge University Press. find
  294. Taboada, Maite and Jack Grieve. 2004. Analyzing Appraisal automatically. In Proceedings of AAAI Spring Symposium on Exploring Attitude and Affect in Text (AAAI Technical Report SS-04-07), pages 158–161, Palo Alto, CA. find
  295. Taboada, Maite and William C. Mann. 2006a. Applications of Rhetorical Structure Theory. Discourse Studies, 8(4):567–588. find
  296. Taboada, Maite and William C. Mann. 2006b. Rhetorical Structure Theory: Looking back and moving ahead. Discourse Studies, 8:423–459. Taboada, Maite and Radoslava Trnavac, editors. 2013. find
  297. Nonveridicality and Evaluation. Brill, Leiden. Taboada, Maite, Kimberly Voll, and Julian Brooke. 2008. Extracting sentiment as a function of discourse structure and topicality. Technical report 2008-20, School of Computing Science. find
  298. Taboada, Maite, Julian Brooke, and Manfred Stede. 2009. Genre-based paragraph classification for sentiment analysis. In Proceedings of the 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 62–70, London, UK. find
  299. Taboada, Maite, Julian Brooke, Milan Tofiloski, Kimberly Voll, and Manfred Stede. 2011. Lexicon-based methods for sentiment analysis. Computational Linguistics, 37:267–307. Täckström, Oscar and Ryan McDonald. 2011. Semi-supervised latent variable models for sentence-level sentiment analysis. In Pr…
  300. Tan, Chenhao, Lillian Lee, Jie Tang, Long Jiang, Ming Zhou, and Ping Li. 2011. User-level sentiment analysis incorporating social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1397–1405, San Diego, CA. find
  301. Teufel, Simone and Marc Moens. 2002. Summarizing scientific articles: Experiments with relevance and rhetorical structure. Computational Linguistics, 28(4):409–445. find
  302. Thelwall, Mike, David Wilkinson, and Sukhvinder Uppal. 2010. Data mining emotion in social network communication: Gender differences in MySpace. Journal of the American Society for Information Science and Technology, 61(1):190–199. find
  303. Thomas, Matt, Bo Pang, and Lillian Lee. 2006. Get out the vote: Determining support or opposition from congressional floor-debate transcripts. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 327–335, Sydney. Thompson, Geoff and Laura Alba-Juez, editors. 20…
  304. John Benjamins. Titov, Ivan and Ryan McDonald. 2008. A joint model of text and aspect ratings for sentiment summarization. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, pages 308–316, Columbus, OH.
  305. Toprak, Cigdem, Niklas Jakob, and Iryna Gurevych. 2010. Sentence and expression level annotation of opinions in user-generated discourse. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 575–584, Upsala.
  306. Trivedi, Rakshit S. and Jacob Eisenstein. 2013. Discourse connectors for latent subjectivity in sentiment analysis. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, pages 808–813, Atlanta, GA. find
  307. Trnavac, Radoslava and Maite Taboada. 2012. The contribution of nonveridical rhetorical relations to evaluation in discourse. Language Sciences, 34 (3):301–318. find
  308. Tsur, Oren, Dmitry Davidov, and Ari Rappoport. 2010. ICWSM-A great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews. In Proceedings of the Fourth International Conference on Weblogs and Social Media, pages 162–169, Washington, DC. find
  309. Turney, Peter D. and Michael L. Littman. 2002. Unsupervised learning of semantic orientation from a hundred-billion-word corpus. Technical report EGB-1094, National Research Council Canada. find
  310. Turney, Peter D. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Meeting of the Association for Computational Linguistics, pages 417–424, Philadelphia, PA.
  311. Utsumi, Akira. 1996. A unified theory of irony and its computational formalization. In Proceedings of the 16th Conference on Computational Linguistics, pages 962–967, Sydney.
  312. Utsumi, Akira. 2004. Stylistic and contextual effects in irony processing. In Proceedings of the 26th Annual Meeting of the Cognitive Science Society, pages 1369–1374, Chicago, IL. Van de Kauter, Marjan, Bart Desmet, and Véronique Hoste. 2015. The good, the bad and the implicit: A comprehensive app… find
  313. Vanzo, Andrea, Danilo Croce, and Roberto Basili. 2014. A context-based model for sentiment analysis in Twitter. In Proceedings of the International Conference on Computational Linguistics, pages 2345–2354, Dublin. find
  314. Veale, Tony and Yanfen Hao. 2010. Detecting ironic intent in creative comparisons. In Proceedings of the European Conference on Artificial Intelligence, pages 765–770, Lisbon. find
  315. Velldall, Erik, Lilja Ovrelid, Jonathon Read, and Stephan Oepen. 2012. Speculation and negation: Rules, rankers, and the role of syntax. Computational Linguistics, 38(2):369–410. Vincent, Marc and Grégoire Winterstein. 2014. Argumentative insights from an opinion classification task on a French cor…
  316. Vo, Duy-Tin and Yue Zhang. 2015. Target-dependent Twitter sentiment classification with rich automatic features. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, pages 1347–1353, Buenos Aires, Argentina. find
  317. Voas, David. 2014. Towards a sociology of attitudes. Sociological Research Online, 19(1):12. find
  318. Volkova, Svitlana, Glen Coppersmith, and Benjamin Van Durme. 2014. Inferring user political preferences from streaming communications. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 186–196, Baltimore, MD. find
  319. Volkova, Svitlana, Yoram Bachrach, Michael Armstrong, and Vijay Sharma. 2015. find
  320. Inferring latent user properties from texts published in social media. In Proceedings of the Twenty-Ninth Conference on Artificial Intelligence, pages 4296–4297, Austin, TX. Voll, Kimberly and Maite Taboada. 2007. Not all words are created equal: Extracting semantic orientation as a function of adje… find
  321. Wachsmuth, Henning, Martin Trenkmann, Benno Stein, and Gregor Engels. 2014. Modeling review argumentation for robust sentiment analysis. In Proceedings of the International Conference on Computational Linguistics, pages 553–564, Dublin. find
  322. Wallace, Byron C., Do Kook Choe, and Eugene Charniak. 2015. Sparse, contextually informed models for irony detection: Exploiting user communities, entities and sentiment. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confer…
  323. Wallace, Byron C. 2015. Computational irony: A survey and new perspectives. Artificial Intelligence Review, 43(4):467–483. find
  324. Walton, Douglas. 2009. Argumentation theory: A very short introduction. In Guillermo Simari and Iyad Rahwan, editors, Argumentation in Artificial Intelligence. Springer, Berlin, pages 1–22. find
  325. Wan, Xiaojun. 2009. Co-training for cross-lingual sentiment classification. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing, pages 235–243, Singapore.
  326. Wang, Hao and Martin Ester. 2014. A sentiment-aligned topic model for product aspect rating prediction. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1192–1202, Doha. find
  327. Wang, Jian and Yi Zhang. 2013. Opportunity model for e-commerce recommendation: Right product; right time. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013, pages 303–312, Dublin. find
  328. Wang, Chunyan, Mao Ye, and Bernardo A. Huberman. 2012. From user comments to on-line conversations. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 244–252, Beijing. find
  329. Wang, Jinpeng, Gao Cong, Wayne Xin Zhao, and Xiaoming Li. 2015. Mining user intents in Twitter: A semi-supervised approach to inferring intent categories for tweets. In Proceedings of the Twenty-Ninth Conference on Artificial Intelligence, pages 318–324, Austin, TX. find
  330. Wang, Po-Ya Angela. 2013. \#irony or \#sarcasm-a quantitative and qualitative study based on Twitter. In Proceedings of the Pacific Asia Conference on Language, Information and Computation, pages 349–356, Taipei.
  331. Webber, Bonnie, Markus Egg, and Valia Kordoni. 2012. Discourse structure and language technology. Natural Language Engineering, 18(4):437–490. find
  332. West, Robert, Hristo S. Paskov, Jure Leskovec, and Christopher Potts. 2014. Exploiting social network structure for person-to-person sentiment analysis. Transactions of the Association for Computational Linguistics, 2:297–310. find
  333. White, Peter R. R. 2003. Beyond modality and hedging: A dialogic view of the language of intersubjective stance. Text, 23(2):259–284. find
  334. White, Peter R. R. 2004. Subjectivity, evaluation and point of view in media discourse. In Caroline Coffin, Ann Hewings, and Kay O’Halloran, editors, Applying English Grammar: Corpus and Functional Approaches. Arnold, London, pages 229–246. find
  335. White, Peter R. R. 2012. An introductory course in Appraisal analysis. http://languageofevaluation.info/appraisal/. [Accessed 28 June 2016]. ↗
  336. Whitelaw, Casey, Navendu Garg, and Shlomo Argamon. 2005. Using Appraisal groups for sentiment analysis. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pages 625–631, Bremen. find
  337. Wiebe, Janyce and Lingjia Deng. 2014. An account of opinion implicatures. arXiv, 1404.6491v1.
  338. Wiebe, Janyce and Ellen Riloff. 2005. Creating subjective and objective sentence classifiers from unannotated texts. In Proceedings of the International Conference on Intelligent Text Processing and Computational Linguistics, volume 3406 of Lecture Notes in Computer Science, pages 486–497. find
  339. Wiebe, Janyce, Eric Breck, Chris Buckley, Claire Cardie, Paul Davis, Bruce Fraser, Diane J. Litman, David R. Pierce, Ellen Riloff, Theresa Wilson, David Day, and Mark Maybury. 2003. Recognizing and organizing opinions expressed in the world press. In Working Notes of the AAAI Spring Symposium in New… find
  340. Wiebe, Janyce, Theresa Wilson, Rebecca Bruce, Matthew Bell, and Melanie Martin. 2004. Learning subjective language. Computational Linguistics, 30(3):277–308.
  341. Wiebe, Janyce, Theresa Wilson, and Claire Cardie. 2005. Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 39(2–3):165–210. find
  342. Wiebe, Janyce M. 1994. Tracking point of view in narrative. Computational Linguistics, 20(2):233–287.
  343. Wiegand, Michael and Dietrich Klakow. 2010. Convolution kernels for opinion holder extraction. In Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 795–803, Los Angeles, CA. Wiegand, Michael, Alexandra Balahur, Be… find
  344. Speech Act Verbs. Academic Press, Sydney. Wilson, Theresa and Janyce Wiebe. 2005. Annotating attributions and private states. In Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky, pages 53–60, Ann Arbor, MI. find
  345. Wilson, Theresa, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pages 347–354, Vancouver.
  346. Wilson, Theresa, Janyce Wiebe, and Rebecca Hwa. 2006. Recognizing strong and weak opinion clauses. Computational Intelligence, 22(2):73–99. find
  347. Wilson, Theresa, Janyce Wiebe, and Paul Hoffmann. 2009. Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistics, 35(3):399–433.
  348. Wilson, Theresas. 2008. Annotating subjective content in meetings. In Proceedings of the 6th International Conference on Language Resources and Evaluation, pages 2738–2745, Marrakech. find
  349. Winokur, Jon. 2005. The Big Book of Irony. find
  350. St. Martin’s Press, New York. Wooldridge, Michael J. 2000. find
  351. Reasoning About Rational Agents. MIT Press, Cambridge, MA. Wu, Yuanbin, Qi Zhang, Xuanjing Huang, and Lide Wu. 2009. Phrase dependency parsing for opinion mining. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1533–1541, Singapore.
  352. Wu, Yuanbin, Qi Zhang, Xuanjing Huang, and Lide Wu. 2011. Structural opinion mining for graph-based sentiment representation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1332–1341, Edinburgh. find
  353. Xia, Rui and Chengqing Zong. 2010. Exploring the use of word relation features for sentiment classification. In Proceedings of the 23rd International Conference on Computational Linguistics, pages 1336–1344, Beijing. find
  354. Xia, Rui, Chengqing Zong, Xuelei Hu, and Erik Cambria. 2015. Feature ensemble plus sample selection: Domain adaptation for sentiment classification (extended abstract). In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, pages 4229–4233, Buenos Aires. find
  355. Xu, Kaiquan, Jiexun Li, and Stephen Shaoyi Liao. 2011. Sentiment community detection in social networks. In Proceedings of the 2011 iConference, pages 804–805, Seattle, WA. find
  356. Yang, Bishan and Claire Cardie. 2012. Extracting opinion expressions with semi-Markov conditional random fields. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 1335–1345, Jeju Island. find
  357. Yang, Bishan and Claire Cardie. 2014. Context-aware learning for sentence-level sentiment analysis with posterior regularization. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 325–335, Baltimore, MD. find
  358. Yang, Seon and Youngjoong Ko. 2011. Extracting comparative entities and predicates from texts using comparative type classification. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 1636–1644, Portland, OR. find
  359. Yang, Min, Wenting Tu, Ziyu Lu, Wenpeng Yin, and Kam-Pui Chow. 2015. LCCT: A semi-supervised model for sentiment classification. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 546–555, Denver, C… find
  360. Yano, Tae, Dani Yogatama, and Noah A. Smith. 2013. A penny for your tweets: Campaign contributions and Capitol Hill microblogging. In Proceedings of the 7th International AAAI Conference on Weblogs and Social Media, pages 737–740, Boston, MA. find
  361. Yessenalina, Ainur and Claire Cardie. 2011. Compositional matrix-space models for sentiment analysis. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 172–182, Edinburgh. find
  362. Yessenalina, Ainur, Yisong Yue, and Claire Cardie. 2010. Multi-level structured models for document-level sentiment classification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1046–1056, Cambridge, MA. find
  363. Yogatama, Dani and Noah Smith. 2014. Making the most of bag of words: Sentence regularization with alternating direction method of multipliers. In Proceedings of the 31st International Conference on Machine Learning, pages 656–664, Beijing. find
  364. Yu, Hong and Hatzivassiloglou Vasileios. 2003. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 129–136, Sapporo.
  365. Zeng, Lingwei and Fang Li. 2013. A classification-based approach for implicit feature identification. In Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data, volume 8202 of Lecture Notes in Computer Science, pages 190–202. find
  366. Springer. Zhang, Lei and Bing Liu. 2011. Identifying noun product features that imply opinions. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 575–580, Portland, OR. find
  367. Zhao, Lili and Chunping Li. 2009. Ontology based opinion mining for movie reviews. In Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management, pages 204–214, Vienna. find
  368. Zhao, Wayne Xin, Jing Jiang, Hongfei Yan, and Xiaoming Li. 2010. Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 56–65, Cambridge, MA. find
  369. Zhou, Lanjun, Binyang Li, Wei Gao, Zhongyu Wei, and Kam-Fai Wong. 2011. Unsupervised discovery of discourse relations for eliminating intra-sentence polarity ambiguities. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 162–171, Edinburgh.
  370. Zhu, Xiaodan, Hongyu Guo, and Parinaz Sobhani. 2015. Neural networks for integrating compositional and non-compositional sentiment in sentiment composition. In Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, pages 1–9, Denver, CO. find
  371. Zhuang, Li, Feng Jing, and Xiao-Yan Zhu. 2006. Movie review mining and summarization. In Proceedings of the ACM International Conference on Information and Knowledge Management, pages 43–50, Arlington,VA. Zirn, Cäcilia, Mathias Niepert, Heiner Stuckenschmidt, and Michael Strube. 2011. Fine-grained… find
  372. Zwarts, Frans. 1995. Nonveridical contexts. Linguistic Analysis, 25(3/4):286–312. find
  373. This article has been cited by: 1. Maite Taboada, Radoslava Trnavac, Cliff Goddard. 2017. On Being Negative. Corpus Pragmatics 1:1, 57-76. [CrossRef] find

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