Context Extraction for Aspect-Based Sentiment Analytics: Combining Syntactic, Lexical and Sentiment Knowledge

  • Anil Bandhakavi
  • Nirmalie WiratungaEmail author
  • Stewart Massie
  • Rushi Luhar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11311)


Aspect-level sentiment analysis of customer feedback data when done accurately can be leveraged to understand strong and weak performance points of businesses and services and also formulate critical action steps to improve their performance. In this work we focus on aspect-level sentiment classification studying the role of opinion context extraction for a given aspect and the extent to which traditional and neural sentiment classifiers benefit when trained using the opinion context text. We introduce a novel method that combines lexical, syntactical and sentiment knowledge effectively to extract opinion context for aspects. Thereafter we validate the quality of the opinion contexts extracted with human judgments using the BLEU score. Further we evaluate the usefulness of the opinion contexts for aspect-sentiment analysis. Our experiments on benchmark data sets from SemEval and a real-world dataset from the insurance domain suggests that extracting the right opinion context combining syntactical with sentiment co-occurrence knowledge leads to the best aspect-sentiment classification performance. From a commercial point of view, accurate aspect extraction, provides an elegant means to identify “pain-points” in a business. Integrating our work into a commercial CX platform ( is enabling the company’s clients to better understand their customer opinions.


Aspect extraction Sentiment analysis Natural language processing Machine learning 


  1. 1.
    Arora, S., Mayfield, E., Penstein-Rosé, C., Nyberg, E.: Sentiment classification using automatically extracted subgraph features. In: NAACL-HLT Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 131–139 (2010)Google Scholar
  2. 2.
    Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)
  3. 3.
    Brychcın, T., Konkol, M., Steinberger, J.: Uwb: machine learning approach to aspect-based sentiment analysis. In: SemEval 2014, p. 817 (2014)Google Scholar
  4. 4.
    Chen, Y.Y., Wiratunga, N., Lothian, R.: Effective dependency rule-based aspect extraction for social recommender systems. In: 21st Pacific Asia Conference on Information Systems (2017)Google Scholar
  5. 5.
    Fei, G., Chen, Z., Liu, B.: Review topic discovery with phrases using the polya urn model. In: COLING, pp. 667–676 (2014)Google Scholar
  6. 6.
    Garcıa-Pablos, A., Cuadros, M., Rigau, G.: V3: unsupervised aspect based sentiment analysis for semeval-2015 task 12. In: SemEval-2015 (2015)Google Scholar
  7. 7.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Processing (2009)Google Scholar
  8. 8.
    Hazem, A., Morin, E.: Improving bilingual lexicon extraction from comparable corpora using window-based and syntax-based models. In: Gelbukh, A. (ed.) CICLing 2014. LNCS, vol. 8404, pp. 310–323. Springer, Heidelberg (2014). Scholar
  9. 9.
    Hu, M., Liu, B.: Mining opinion features in customer reviews. In: Proceedings of the 19th National Conference on AI 2004 (2004)Google Scholar
  10. 10.
    Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)
  11. 11.
    Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the Conference on Empirical Methods in NLP (EMNLP), pp. 1746–1751 (2014)Google Scholar
  12. 12.
    Kiritchenko, S., Zhu, X.D., Cherry, C., Mohammad, S.: NRC-Canada-2014: detecting aspects and sentiment in customer reviews. In: SemEval@COLING (2014)Google Scholar
  13. 13.
    Laddha, A., Mukherjee, A.: Extracting aspect specific opinion expressions. In: Proceedings of the Conference on Empirical Methods in NLP, pp. 6270–637 (2016)Google Scholar
  14. 14.
    Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. In: Proceedings of the 7th International Workshop on Semantic Evaluation, pp. 321–327 (2013)Google Scholar
  15. 15.
    Muhammad, A., Wiratunga, N., Lothian, R.: Contextual sentiment analysis for social media genres. Knowl.-Based Syst. 108, 92–101 (2016)CrossRefGoogle Scholar
  16. 16.
    Nakagawa, T., Inui, K., Kurohashi, S.: Dependency tree-based sentiment classification using CRFs with hidden variables. In: Human Language Technologies: Conference of the North American Chapter of the ACL, pp. 786–794 (2010)Google Scholar
  17. 17.
    Nandan, N., Dahlmeier, D., Vij, A., Malhotra, N.: SAP-RI: a constrained and supervised approach for aspect-based sentiment analysis. In: SemEval 2014, p. 517 (2014)Google Scholar
  18. 18.
    Gamallo Otero, P.: Comparing window and syntax based strategies for semantic extraction. In: Teixeira, A., de Lima, V.L.S., de Oliveira, L.C., Quaresma, P. (eds.) PROPOR 2008. LNCS (LNAI), vol. 5190, pp. 41–50. Springer, Heidelberg (2008). Scholar
  19. 19.
    Ribeiro, F.N., Araujo, M., Goncalves, P., Goncalves, M.A., Benevenuto, F.: Sentibench- a benchmark comparision of state-of-the-paractice sentiment analysis methods. EPJ Data Sci. 5, 23 (2016)CrossRefGoogle Scholar
  20. 20.
    Schouten, K., Frasincar, F., de Jong, F.: COMMIT-P1WP3: a co-occurrence based approach to aspect-level sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) (2014)Google Scholar
  21. 21.
    Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C., Ng, A., Potts, C.: Recursive deep models for semantic compositionality over a seniment treebank. In: Proceedings of the EMNLP (2013)Google Scholar
  22. 22.
    Toh, Z., Wang, W.: DLIREC: aspect term extraction and term polarity classification system. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 235–240 (2014)Google Scholar
  23. 23.
    Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: Proceedings of the Conference on Empirical Methods in NLP, vol. 3, pp. 1533–1541 (2009)Google Scholar
  24. 24.
    Zhao, W.X., et al.: Topical keyphrase extraction from twitter. In: Proceedings of the 49th Annual Meeting of the ACL: Human Language Technologies-Volume 1, pp. 379–388 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Anil Bandhakavi
    • 1
  • Nirmalie Wiratunga
    • 1
    Email author
  • Stewart Massie
    • 1
  • Rushi Luhar
    • 2
  1. 1.School of ComputingRobert Gordon UniversityAberdeenUK
  2. 2.SentiSumLondonUK

Personalised recommendations