Skip to main content
Springer Nature Link
Log in

Exploring overall opinions for document level sentiment classification with structural SVM

  • Special Issue Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

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.

References

  1. Sang, J., Xu, C.: Right buddy makes the difference: an early exploration of social relation analysis in multimedia applications. In: ACM International Conference on Multimedia, pp. 19–28. ACM (2012)

  2. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)

    Article  Google Scholar 

  3. McAuley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on Recommender systems, pp. 165–172. ACM (2013)

  4. Sang, J., Xu, C., Liu, J.: User-aware image tag refinement via ternary semantic analysis. IEEE Trans. Multimed. 14(3), 883–895 (2012)

    Article  Google Scholar 

  5. Sang, J.: User-centric cross-osn multimedia computing. In: Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, MM ’15, Brisbane, Australia, October 26–30, 2015, pp. 1333–1334 (2015). doi:10.1145/2733373.2807423

  6. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012). doi:10.2200/S00416ED1V01Y201204HLT016

    Article  Google Scholar 

  7. Fang, Q., Xu, C., Sang, J., Hossain, M.S., Ghulam, M.: Word-of-mouth understanding: Entity-centric multimodal aspect-opinion mining in social media. IEEE Trans. Multimed. 17(12), 2281–2296 (2015). doi:10.1109/TMM.2015.2491019

  8. Turney, P.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 417–424 (2002)

  9. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)

    Article  Google Scholar 

  10. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)

  11. Wang, S., Manning, C.: Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th annual meeting of the association for computational linguistics (volume 2: Short Papers), pp. 90–94. Association for Computational Linguistics, Jeju Island, Korea (2012)

  12. Yu, C.N.J., Joachims, T.: Learning structural svms with latent variables. In: Proceedings of the 26th annual international conference on machine learning, pp. 1169–1176. ACM (2009)

  13. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’04. ACM, New York, pp. 168–177 (2004). doi:10.1145/1014052.1014073

  14. Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, WSDM ’08, pp. 231–240. ACM, New York (2008). doi:10.1145/1341531.1341561

  15. Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. J. Artif. Int. Res. 50(1), 723–762 (2014)

    Google Scholar 

  16. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  17. Lu, Y., Zhai, C.: Opinion integration through semi-supervised topic modeling. In: Proceedings of the 17th International Conference on World Wide Web, WWW ’08, pp. 121–130. ACM, New York (2008). doi:10.1145/1367497.1367514

  18. Sang, J., Xu, C.: Browse by chunks: topic mining and organizing on web-scale social media. ACM Trans. Multimed. Comput. Commun. Appl. 7(1), 30 (2011)

    Google Scholar 

  19. Lakkaraju, H., Bhattacharyya, C., Bhattacharya, I., Merugu, S.: Exploiting coherence for the simultaneous discovery of latent facets and associated sentiments. In: Proceedings of the 2011 SIAM International Conference on Data Mining (2011)

  20. Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM conference on Information and knowledge management, pp. 375–384. ACM (2009)

  21. Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42th Annual Meeting of the Association for Computational Linguistics (ACL) (2004)

  22. McDonald, R., Hannan, K., Neylon, T., Wells, M., Reynar, J.: Structured models for fine-to-coarse sentiment analysis. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 432–439. Association for Computational Linguistics, Prague, Czech Republic (2007)

  23. Yessenalina, A., Yue, Y., Cardie, C.: Multi-level structured models for document-level sentiment classification. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1046–1056. Association for Computational Linguistics, Cambridge, MA (2010)

  24. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K. (eds.) Advances in neural information processing systems, vol. 26, pp. 3111–3119. Curran Associates Inc, New York, USA (2013)

    Google Scholar 

  25. Baroni, M., Dinu, G., Kruszewski, G.: Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In: Proceedings of the ACL 2014, pp. 238–247. Association for Computational Linguistics (2014)

  26. Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp. 142–150. Association for Computational Linguistics, Portland, Oregon, USA (2011)

  27. Labutov, I., Lipson, H.: Re-embedding words. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 489–493 (2013)

  28. Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the ACL 2014, pp. 1555–1565 (2014)

  29. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st international conference on machine learning, pp. 1188–1196 (2014)

  30. Li, J., Luong, M.T., Jurafsky, D.: A hierarchical neural autoencoder for paragraphs and documents. In: Proceedings of the ACL 2015, pp. 1106–1115 (2015)

  31. Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1422–1432. Association for Computational Linguistics (2015)

  32. Jin, W., Ho, H.H., Srihari, R.K.: Opinionminer: a novel machine learning system for web opinion mining and extraction. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1195–1204. ACM (2009)

  33. Jakob, N., Gurevych, I.: Extracting opinion targets in a single-and cross-domain setting with conditional random fields. In: Proceedings of the 2010 conference on empirical methods in natural language processing, pp. 1035–1045. Association for Computational Linguistics (2010)

  34. Moghaddam, S., Ester, M.: On the design of lda models for aspect-based opinion mining. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM ’12, pp. 803–812. ACM, New York (2012). doi:10.1145/2396761.2396863

  35. Becker, I., Aharonson, V.: Last but definitely not least: On the role of the last sentence in automatic polarity-classification. In: Proceedings of the ACL 2010 Conference Short Papers, pp. 331–335 (2010)

  36. Joachims, T., Finley, T., Yu, C.N.J.: Cutting-plane training of structural svms. Mach. Learn. 77(1), 27–59 (2009). doi:10.1007/s10994-009-5108-8

    Article  MATH  Google Scholar 

  37. Täckström, O., McDonald, R.: Discovering fine-grained sentiment with latent variable structured prediction models. In: Advances in information retrieval, pp. 368–374. Springer, Heidelberg (2011)

  38. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  39. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

  40. Zhang, Y., Er, M.J., Venkatesan, R., Wang, N., Pratama, M.: Sentiment classification using comprehensive attention recurrent models. In: 2016 International joint conference on neural networks (IJCNN), pp. 1562–1569 (2016). doi:10.1109/IJCNN.2016.7727384

  41. Gao, Y., Glowacka, D.: Deep gate recurrent neural network. JMLR Workshop Conf. Proc. 6, 350–365 (2016)

Download references

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.

Author information

Authors and Affiliations

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

    Xiaojia Pu, Gangshan Wu & Chunfeng Yuan

Authors
  1. Xiaojia Pu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Gangshan Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Chunfeng Yuan
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xiaojia Pu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-017-0550-0

Keywords