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Exploiting Discourse Relations for Sentiment Analysis

Summary

The paper addresses document-level polarity classification of Chinese product reviews. Lexicon-style approaches count every clause equally, but discourse structure can shift the overall verdict: a concluding clause introduced by "in general" can outweigh earlier praise. The authors split each review into sub-sentences at punctuation marks. Each sub-sentence gets a polarity from the Basic SELC model of Qiu et al. (2009) and a discourse type (head or modifier of a relation). The types are assigned through explicit connectives drawn from a Chinese discourse relation hierarchy built on Rhetorical Structure Theory (RST), which the authors compiled themselves. A linear-kernel SVM learns one weight per discourse type plus an offset, and the document score is the weighted sum of sub-sentence polarities. The authors compare weighting schemes of increasing granularity and a greedy search that keeps only the most influential relations or connectives. On reviews collected from 360buy, every discourse-aware variant beats the baselines, and the greedy relation method performs best. The study suggests that shallow, connective-based discourse cues improve Chinese sentiment analysis even when no discourse parser is available.

Key points

  • The authors present what they describe as the first fairly complete Chinese discourse relation hierarchy with associated connectives. It includes multi-nucleus relations such as Coordinate and Temporal, and single-nucleus relations such as Contrast, Cause and Condition, each split into H (head) and M (modifier) types.
  • Data come from 360buy reviews, with labels assigned automatically: the "strong points" column counts as positive and the "shortcomings" column as negative. Word-level HowNet scoring reaches only 56.9% F, whereas the sub-sentence baseline reaches 83.55%, rising to 83.86% with the offset.
  • Five weighting variants were tested: SNSS, SNMS, MNMS, GDR and GCW. All outperform both baselines, with significance at p<0.01. MNMS does slightly worse than SNMS, possibly because it has too many weights to train.
  • GDR achieves the best overall F of 85.28%, which is 1.42 points above baseline2. Its greedy search selects Contrast, Cause, Condition and Generalization as the influential relations.
  • GCW relies on just six connectives (e.g., 但是|but, 虽然|although, 总的来说|in general) and still reaches 85.13% F.

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

Fields: Linguistics & languages

Topics: Discourse analysis

Cited by

6 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 4 not yet.

Cite

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

@inproceedings{wang2012exploiting,
  title = {{Exploiting Discourse Relations for Sentiment Analysis}},
  author = {Wang, Fei and Wu, Yunfang and Qiu, Likun},
  booktitle = {Proceedings of COLING 2012: Posters},
  year = {2012},
  url = {https://aclanthology.org/C12-2128/}
}

References

14 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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  2. Hernault, H., Bollegala, D. and Ishizuka, M. (2010). A semi-supervised approach to improve classification of infrequent discourse relations using feature vector extension. In Proceedings of the EMNLP-2010, pages 399-409. find our card
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