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Exploring overall opinions for document level sentiment classification with structural SVM

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Abstract

As a fundamental task of sentiment analysis, document level sentiment classification aims to predict user’s overall sentiment (e.g., positive or negative) towards the target in a document. The document usually consists of various opinion sentences towards different aspects with different sentiments. Therefore, the overall opinion towards the whole target should play a more important role in document sentiment prediction. However, most existing methods for the task treat all sentences of the document equally. Thus, they are easy to encounter difficulty when the sentiments of most aspect opinion sentences are not coherent with the overall sentiment. To address this, we propose a novel method for document sentiment classification which adequately explores the effect of overall opinion sentences. In our method, firstly, multiple features are exploited to recognize candidate overall opinion sentences, and then a structural SVM is utilized to encode the overall opinion sentences for document sentiment classification. Experiments on several public available datasets including product reviews and movie reviews show the effectiveness of our method.

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Notes

  1. This is a review of Canon S100 in the dataset: http://www.cs.uic.edu/~liub/FBS/Reviews-9-products.rar.

  2. https://github.com/oscartackstrom/sentence-sentiment-data.

  3. https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html.

  4. http://snap.stanford.edu/data/web-Amazon.html.

  5. http://www.cs.cornell.edu/people/pabo/movie-review-data/.

  6. http://ai.stanford.edu/~amaas/data/sentiment.

  7. http://mpqa.cs.pitt.edu/lexicons/subj_lexicon

  8. The improvement of our method is significant, since with the paired t test, p < 0.05.

  9. The hyperparameter tuning has great influence on the performance of LSTM, we follow the work[40, 41], and the result is comparable with [40] and better than [41].

  10. The architecture of GRU differs in many works, e.g.,  [31, 40, 41], we follow the work [40, 41], and after fine tuning, the result is better than both of them.

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Acknowledgements

We thank the reviewers for their helpful suggestions. This work is supported by the National Science Foundation of China under Grant No. 61321491 and Collaborative Innovation Center of Novel Software Technology and Industrialization.

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  1. State Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing, China

    Xiaojia Pu, Gangshan Wu & Chunfeng Yuan

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  1. Xiaojia Pu
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Correspondence to Xiaojia Pu.

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Pu, X., Wu, G. & Yuan, C. Exploring overall opinions for document level sentiment classification with structural SVM. Multimedia Systems 25, 21–33 (2019). https://doi.org/10.1007/s00530-017-0550-0

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