Constructing Aspect-Based Sentiment Lexicons with Topic Modeling

  • Elena Tutubalina
  • Sergey NikolenkoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 661)


We study topic models designed to be used for sentiment analysis, i.e., models that extract certain topics (aspects) from a corpus of documents and mine sentiment-related labels related to individual aspects. For both direct applications in sentiment analysis and other uses, it is desirable to have a good lexicon of sentiment words, preferably related to different aspects in the words. We have previously developed a modification for several popular sentiment-related LDA extensions that trains prior hyperparameters \(\beta \) for specific words. We continue this work and show how this approach leads to new aspect-specific lexicons of sentiment words based on a small set of “seed” sentiment words; the lexicons are useful by themselves and lead to improved sentiment classification.


Topic Model Latent Dirichlet Allocation Sentiment Analysis Sentiment Classification Latent Dirichlet Allocation Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the Russian Science Foundation grant no. 15-11-10019. We thank Alexander Panchenko and Nikolay Arefyev for providing us the word2vec model and its Russian-language training data.


  1. 1.
    Neviarouskaya, A., Prendinger, H., Ishizuka, M.: Sentiful: generating a reliable lexicon for sentiment analysis. In: 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, 2009, ACII 2009, pp. 1–6. IEEE (2009)Google Scholar
  2. 2.
    Tang, D., Wei, F., Qin, B., Zhou, M., Liu, T.: Building large-scale twitter-specific sentiment lexicon: a representation learning approach. In: Proceedings of COLING, pp. 172–182 (2014)Google Scholar
  3. 3.
    Severyn, A., Moschitti, A.: On the automatic learning of sentiment lexicons. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL HLT 2015) (2015)Google Scholar
  4. 4.
    Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. J. Artif. Intell. Res. 50, 723–762 (2014)zbMATHGoogle Scholar
  5. 5.
    Vorontsov, K., Frei, O., Apishev, M., Romov, P., Suvorova, M., Yanina, A.: Nonbayesian additive regularization for multimodal topic modeling of large collections. In: Proceedings of the 2015 Workshop on Topic Models: Post-Processing and Applications, TM 2015, New York, NY, USA, pp. 29–37. ACM (2015)Google Scholar
  6. 6.
    Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, Cambridge (2015)CrossRefGoogle Scholar
  7. 7.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)Google Scholar
  8. 8.
    Chernyshevich, M.: Ihs r&d belarus: cross-domain extraction of product features using conditional random fields. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 309–313 (2014)Google Scholar
  9. 9.
    Ivanov, V., Tutubalina, E., Mingazov, N., Alimova, I.: Extracting aspects, sentiment and categories of aspects in user reviews about restaurants and cars. In: Proceedings of International Conference Dialog, pp. 22–34 (2015)Google Scholar
  10. 10.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  11. 11.
    Blinov, P.D., Kotelnikov, E.V.: Semantic similarity for aspect-based sentiment analysis. In: Proceedings of the 21st International Conference on Computational Linguistics Dialog-2015, vol. 2, pp. 36–45 (2015)Google Scholar
  12. 12.
    Tarasov, D.S.: Deep recurrent neural networks for multiple language aspect-based sentiment analysis of user reviews. In: Proceedings of the 21st International Conference on Computational Linguistics Dialog-2015, vol. 2 (2015)Google Scholar
  13. 13.
    Lin, C., He, Y., Everson, R., Ruger, S.: Weakly supervised joint sentiment-topic detection from text. IEEE Trans. Knowl. Data Eng. 24, 1134–1145 (2012)CrossRefGoogle Scholar
  14. 14.
    Jo, Y., Oh, A.H.: Aspect and sentiment unification model for online review analysis. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, New York, NY, USA, pp. 815–824. ACM (2011)Google Scholar
  15. 15.
    Yang, Z., Kotov, A., Mohan, A., Lu, S.: Parametric and non-parametric user-aware sentiment topic models. In: Proceedings of the 38th ACM SIGIR (2015)Google Scholar
  16. 16.
    Lu, B., Ott, M., Cardie, C., Tsou, B.: Multi-aspect sentiment analysis with topic models. In: 2011 IEEE 11th International Conference Data Mining Workshops (ICDMW), pp. 81–88 (2011)Google Scholar
  17. 17.
    Kim, S., Zhang, J., Chen, Z., Oh, A.H., Liu, S.: A hierarchical aspect-sentiment model for online reviews. In: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, Bellevue, Washington, USA, 14–18 July 2013 (2013)Google Scholar
  18. 18.
    Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics, pp. 174–181. ACL (1997)Google Scholar
  19. 19.
    Chetviorkin, I., Loukachevitch, N.V.: Extraction of Russian sentiment lexicon for product meta-domain. In: COLING, pp. 593–610. Citeseer (2012)Google Scholar
  20. 20.
    Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424. Association for Computational Linguistics (2002)Google Scholar
  21. 21.
    Tutubalina, E., Nikolenko, S.: Inferring sentiment-based priors in topic models. In: Lagunas, O.P., Alcántara, O.H., Figueroa, G.A. (eds.) MICAI 2015. LNCS (LNAI), vol. 9414, pp. 92–104. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-27101-9_7 CrossRefGoogle Scholar
  22. 22.
    Al-Rfou, R., Perozzi, B., Skiena, S.: Polyglot: distributed word representations for multilingual NLP. In: Proceedings of the Seventeenth Conference on Computational Natural Language Learning, Sofia, Bulgaria, pp. 183–192. Association for Computational Linguistics (2013)Google Scholar
  23. 23.
    Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1532–1543. Association for Computational Linguistics (2014)Google Scholar
  24. 24.
    Arefyev, N., Panchenko, A., Lukanin, A., Lesota, O., Romanov, P.: Evaluating three corpus-based semantic similarity systems for Russian. In: Proceedings of International Conference on Computational Linguistics Dialogue (2015)Google Scholar
  25. 25.
    Panchenko, A., Loukachevitch, N., Ustalov, D., Paperno, D., Meyer, C.M., Konstantinova, N.: Russe: the first workshop on russian semantic similarity. In: Proceedings of the International Conference on Computational Linguistics and Intellectual Technologies (Dialogue), pp. 89–105 (2015)Google Scholar
  26. 26.
    Loukachevitch, N., Blinov, P., Kotelnikov, E., Rubtsova Yu, V., Ivanov, V., Tutubalina, E.: Sentirueval: testing object-oriented sentiment analysis systems in Russian. In: Proceedings of International Conference Dialog, pp. 3–9 (2015)Google Scholar
  27. 27.
    Hagen, M., Potthast, M., Büchner, M., Stein, B.: Twitter sentiment detection via ensemble classification using averaged confidence scores. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 741–754. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-16354-3_81 Google Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Kazan (Volga Region) Federal UniversityKazanRussia
  2. 2.Steklov Institute of Mathematics at St. PetersburgSt. PetersburgRussia
  3. 3.Laboratory for Internet Studies, NRU Higher School of EconomicsSt. PetersburgRussia
  4. 4.Deloitte Analytics InstituteMoscowRussia

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