Modelling Context with User Embeddings for Sarcasm Detection in Social Media
Summary
Sarcasm is hard to detect from wording alone, because the same sentence can be sincere or ironic depending on who writes it. Earlier systems added information about the speaker through hand-built features such as sentiment lexicons, profile data and behavioural traits. Those features take a lot of work to build and rely on external tools. The authors replace them with learned user embeddings. Each Twitter author gets a vector trained only on the text of their earlier posts, using an objective similar to Paragraph Vector: the model predicts a user's words from that user's vector. The model, CUE-CNN, encodes the tweet with a convolutional network, combines that encoding with the author's vector, and passes both through a hidden layer before classification. Its accuracy is more than 2 points higher than the feature-rich model of Bamman and Smith (2015), and its results vary less across cross-validation folds. The authors also show that the learned user vectors reflect real user traits. The main point is that speaker context can be learned automatically instead of hand-engineered.
Key points
- CUE-CNN makes its prediction from two inputs together: CNN features of the tweet (ReLU filters, concatenated) and an embedding of the author.
- User embeddings are pre-trained with a hinge-loss objective over word usage and negative sampling. Negatives drawn from a unigram distribution over all users' posts gave 0.8% more accuracy than uniformly random negatives.
- Each step helped: adding author context, pre-training the user vectors, and adding a hidden layer that models how user and content interact.
- In a t-SNE plot, users fall into groups by political leaning (based on following Democratic vs. Republican politicians) and by interest in sports. This suggests the vectors capture homophily, meaning similar users end up close together.
- Training used cross-entropy loss with AdaDelta. Word vectors were learned with Hierarchical Softmax as implemented in Gensim.
Cited by
- Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection
- Detecting Sarcasm in Conversation Context Using Transformer-Based Models
- Affective and Contextual Embedding for Sarcasm Detection
- Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model
- Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)
- “When Numbers Matter!”: Detecting Sarcasm in Numerical Portions of Text
- Linguistic Features in Automatic Sarcasm Detection
- From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset
- Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic
- Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm Detection
- SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic
- Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal
- Sarcasm Analysis Using Conversation Context
- Interpreting Verbal Irony: Linguistic Strategies and the Connection to the Type of Semantic Incongruity
- A Report on the 2020 Sarcasm Detection Shared Task
- CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
- SemEval-2018 Task 3: Irony Detection in English Tweets
- Automatic Sarcasm Detection: A Survey
- A Large Self-Annotated Corpus for Sarcasm
- Representing Social Media Users for Sarcasm Detection
- Exploring Author Context for Detecting Intended vs Perceived Sarcasm
- iSarcasm: A Dataset of Intended Sarcasm
- Chandler: An Explainable Sarcastic Response Generator
- A Transformer-based approach to Irony and Sarcasm detection
- A Novel Hierarchical BERT Architecture for Sarcasm Detection
- Reasoning with Sarcasm by Reading In-Between
- Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text Data
- Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association
- Tweet Sarcasm Detection Using Deep Neural Network
Cite
@inproceedings{amir2016modelling,
title = {{Modelling Context with User Embeddings for Sarcasm Detection in Social Media}},
author = {Amir, Silvio and Wallace, Byron C. and Lyu, Hao and Carvalho, Paula and Silva, Mário J.},
booktitle = {Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning},
year = {2016},
doi = {10.18653/v1/k16-1017},
eprint = {1607.00976},
archivePrefix = {arXiv},
url = {https://doi.org/10.18653/v1/k16-1017}
}References
- Ramón Astudillo, Silvio Amir, Wang Ling, Mario Silva, and Isabel Trancoso. 2015. Learning word representations from scarce and noisy data with embedding subspaces. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1074–1084, Beijing, China, July. find
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