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Text Mining for Word Sentiment Detection

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Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 914))

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Abstract

This work presents a novel approach for automatically generating a sentiment lexicon. We employ an unsupervised learning approach using several probabilistic and information theoretic models. While most of the unsupervised approaches require a set of seed words to begin their work, our methods differ from these by using no a priori knowledge. In addition, our models are effective with a diverse corpus rather than requiring a corpus for a limited domain. We demonstrate the effectiveness of our approaches by performing sentiment analysis on Amazon products reviews, comparing the various automatically-generated lexicons. Based on our cross validation results, we show that our lexicons outperform a widely-used sentiment lexicon on both balanced and unbalanced datasets.

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Authors and Affiliations

  1. University of Arkansas, Fayetteville, AR, 72701, USA

    Kevin Labille, Susan Gauch & Sultan Alfarhood

Authors
  1. Kevin Labille
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  2. Susan Gauch
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  3. Sultan Alfarhood
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Corresponding author

Correspondence to Kevin Labille .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

  2. Department of Software Technology, Delft University of Technology, Voorburg, Zuid-Holland, The Netherlands

    Jan Dietz

  3. Faculty of Exact Sciences and Engineering, University of Madeira, Funchal, Portugal

    David Aveiro

  4. Henley Business School, University of Reading, Reading, UK

    Kecheng Liu

  5. University of Coimbra, Coimbra, Portugal

    Jorge Bernardino

  6. Instituto Politecnico de Setúbal (IPS), Setúbal, Portugal

    Joaquim Filipe

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Labille, K., Gauch, S., Alfarhood, S. (2019). Text Mining for Word Sentiment Detection. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2016. Communications in Computer and Information Science, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-319-99701-8_7

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  • DOI: https://doi.org/10.1007/978-3-319-99701-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99700-1

  • Online ISBN: 978-3-319-99701-8

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