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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)

Abstract

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.

Keywords

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.

Notes

Acknowledgements

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.

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Copyright information

© 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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