Expanding Sentiment Lexicon with Multi-word Terms for Domain-Specific Sentiment Analysis

  • Sang-Sang TanEmail author
  • Jin-Cheon Na
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10075)


The increasing interest to extract valuable information from networked data has heightened the need for effective and reliable sentiment analysis techniques. To this end, lexicon-based sentiment classification has been extensively studied by the research community. However, little is known about the usefulness of different multi-word constructs in creating domain-specific sentiment lexicons. Thus, our primary objective in this paper is to evaluate the performance of bigram, typed dependency, and concept as multi-word lexical entries for domain-specific sentiment classification. Pointwise Mutual Information (PMI) was adopted to select the lexical entries and to calculate the sentiment scores of the multi-word terms. With the features generated from the domain lexicons, a series of experiments were carried out using support vector machine (SVM) classifiers. While all the domain-specific classifiers outperformed the baseline classifier, our results showed that lexicons consisting of bigram entries and typed dependency entries improved the performance to a greater extent.


Sentiment analysis Sentiment lexicon Machine learning Sentiment classification 


  1. 1.
    Tan, S., Cheng, X., Wang, Y., Xu, H.: Adapting naive bayes to domain adaptation for sentiment analysis. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 337–349. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-00958-7_31 CrossRefGoogle Scholar
  2. 2.
    Aue, A., Gamon, M.: Customizing Sentiment Classifiers to New Domains: A Case Study. Technical report (2005)Google Scholar
  3. 3.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354. ACL, Stroudsburg (2005)Google Scholar
  4. 4.
    Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: International Conference on Language Resources and Evaluation, pp. 2200–2204. ELRA, Paris (2010)Google Scholar
  5. 5.
    Lu, Y., Castellanos, M., Dayal, U., Zhai, C.: Automatic construction of a context-aware sentiment lexicon: an optimization approach. In: 20th International Conference on World Wide Web, pp. 347–356. ACM, New York (2011)Google Scholar
  6. 6.
    Qiu, G., Liu, B., Bu, J., Chen, C.: Expanding domain sentiment lexicon through double propagation. In: 21st International Joint Conference on Artificial Intelligence, pp. 1199–1204. Morgan Kaufmann, San Francisco (2009)Google Scholar
  7. 7.
    Kanayama, H., Nasukawa, T.: Fully automatic lexicon expansion for domain-oriented sentiment analysis. In: Conference on Empirical Methods in Natural Language Processing, pp. 355–363. ACL, Stroudsburg (2006)Google Scholar
  8. 8.
    Turney, P., Littman, M.L.: Unsupervised learning of semantic orientation from a hundred-billion-word corpus. Technical report (2002)Google Scholar
  9. 9.
    Zhang, L., Ghosh, R., Dekhil, M., Hsu, M., Liu, B.: Combining lexicon-based and learning-based methods for twitter sentiment analysis. Technical report (2011)Google Scholar
  10. 10.
    Gindl, S., Weichselbraun, A., Scharl, A.: Cross-domain contextualisation of sentiment lexicons. In: 19th European Conference on Artificial Intelligence. WebLyzard, Vienna (2010)Google Scholar
  11. 11.
    Weichselbraun, A., Gindl, S., Scharl, A.: Extracting and grounding context-aware sentiment lexicons. IEEE Intell. Syst. 28, 39–46 (2013)CrossRefGoogle Scholar
  12. 12.
    Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: International Conference on Web Search and Data Mining, pp. 231–240. ACM, New York (2008)Google Scholar
  13. 13.
    Agarwal, B., Poria, S., Mittal, N., Gelbukh, A., Hussain, A.: Concept-level sentiment analysis with dependency-based semantic parsing: a novel approach. Cogn. Comput. 7, 487–499 (2015)CrossRefGoogle Scholar
  14. 14.
    Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. J. Artif. Intell. Res. 50, 723–762 (2014)zbMATHGoogle Scholar
  15. 15.
    Kaji, N., Kitsuregawa, M.: Building lexicon for sentiment analysis from massive collection of HTML documents. In: Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1075–1083. ACL, Stroudsburg (2007)Google Scholar
  16. 16.
    Wang, S., Manning, C.D.: Baselines and bigrams: simple, good sentiment and topic classification. In: 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, pp. 90–94. ACL, Stroudsburg (2012)Google Scholar
  17. 17.
    De Marneffe, M.C., Manning, C.D.: Stanford typed dependencies manual. Technical report (2008)Google Scholar
  18. 18.
    Cambria, E., White, B.: Jumping NLP curves: a review of natural language processing research. IEEE Comput. Intell. Mag. 9, 48–57 (2014)CrossRefGoogle Scholar
  19. 19.
    Poria, S., Agarwal, B., Gelbukh, A., Hussain, A., Howard, N.: Dependency-based semantic parsing for concept-level text analysis. In: Gelbukh, A. (ed.) CICLing 2014. LNCS, vol. 8403, pp. 113–127. Springer, Heidelberg (2014). doi: 10.1007/978-3-642-54906-9_10 CrossRefGoogle Scholar
  20. 20.
    Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: 43rd Annual Meeting on Association for Computational Linguistics, pp. 115–124. ACL, Stroudsburg (2005)Google Scholar
  21. 21.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: ACL 2002 Conference on Empirical Methods in Natural Language Processing, pp. 79–86. ACL, Stroudsburg (2002)Google Scholar
  22. 22.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM, New York (2004)Google Scholar
  23. 23.
    Nakagawa, T., Inui, K. and Kurohashi, S.: Dependency tree-based sentiment classification using CRFs with hidden variables. In: Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 786–794. ACL, Stroudsburg (2010)Google Scholar
  24. 24.
    Arora, S., Mayfield, E., Penstein-Rosé, C., Nyberg, E.: Sentiment classification using automatically extracted subgraph features. In: NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 131–139. ACL, Stroudsburg (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Wee Kim Wee School of Communication and InformationNanyang Technological UniversitySingaporeSingapore

Personalised recommendations