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Inferring Sentiment-Based Priors in Topic Models

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Advances in Artificial Intelligence and Its Applications (MICAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9414))

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Abstract

Over the recent years, several topic models have appeared that are specifically tailored for sentiment analysis, including the Joint Sentiment/Topic model, Aspect and Sentiment Unification Model, and User-Sentiment Topic Model. Most of these models incorporate sentiment knowledge in the \(\beta \) priors; however, these priors are usually set from a dictionary and completely rely on previous domain knowledge to identify positive and negative words. In this work, we show a new approach to automatically infer sentiment-based \(\beta \) priors in topic models for sentiment analysis and opinion mining; the approach is based on the EM algorithm. We show that this method leads to significant improvements for sentiment analysis in known topic models and also can be used to update sentiment dictionaries with new positive and negative words.

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Notes

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Acknowledgements

This work was supported by the Russian Science Foundation grant no. 15-11-10019.

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Authors and Affiliations

  1. Kazan (Volga Region) Federal University, Kazan, Russia

    Elena Tutubalina & Sergey Nikolenko

  2. Steklov Institute of Mathematics, St. Petersburg, Russia

    Sergey Nikolenko

  3. Laboratory for Internet Studies, National Research University-Higher School of Economics, St. Petersburg, Russia

    Sergey Nikolenko

Authors
  1. Elena Tutubalina
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  2. Sergey Nikolenko
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Correspondence to Sergey Nikolenko .

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Editors and Affiliations

  1. Unidad Profesional Interdisciplinaria, México DF, Mexico

    Obdulia Pichardo Lagunas

  2. Universidad Autónoma Metropolitana, México DF, Mexico

    Oscar Herrera Alcántara

  3. Instituto de Investigaciones Eléctricas, Cuernavaca, Morelos, Mexico

    Gustavo Arroyo Figueroa

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Tutubalina, E., Nikolenko, S. (2015). Inferring Sentiment-Based Priors in Topic Models. In: Pichardo Lagunas, O., Herrera Alcántara, O., Arroyo Figueroa, G. (eds) Advances in Artificial Intelligence and Its Applications. MICAI 2015. Lecture Notes in Computer Science(), vol 9414. Springer, Cham. https://doi.org/10.1007/978-3-319-27101-9_7

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  • DOI: https://doi.org/10.1007/978-3-319-27101-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27100-2

  • Online ISBN: 978-3-319-27101-9

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