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Analyzing Text Data for Opinion Mining

  • Conference paper
Natural Language Processing and Information Systems (NLDB 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6716))

Abstract

Opinion mining has become a hot topic at the crossroads of information retrieval and computational linguistics. In this paper, we propose to study two key research problems of designing an opinion mining system, i.e., entity-related opinion detection problem and sentiment analysis problem. For the entity-related opinion detection problem, we want to use sophisticated statistical models, e.g., probabilistic topic models and statistical rule generation methods, to achieve better performance than existing baselines. For the sentiment analysis problem, we have proposed a novel HL-SOT approach and reported its feasibility in an academic publication. Since the kernel classifier utilized in the HL-SOT approach is a linear function, we are working on developing a multi-layer neural network kernel algorithm which results in a non-linear classifier and is expected to improve the performance of the original HL-SOT approach to sentiment analysis.

Ph.D. advisor: Prof. Jon Atle Gulla, Department of Computer and Information Science, Norwegian University of Science and Technology, jag@idi.ntnu.no

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Norwegian University of Science and Technology, Norway

    Wei Wei

Authors
  1. Wei Wei
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Editor information

Editors and Affiliations

  1. School of Computing, University of Alicante, 03080, Alicante, Spain

    Rafael Muñoz

  2. Department of Software and Computing Systems, University of Alicante, Aptdo. de Correos 99, 03080, Alicante, Spain

    Andrés Montoyo

  3. CNAM- Laboratoire Cédric, 292 Rue St. Martin, 75141, Paris Cedex 03, France

    Elisabeth Métais

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Wei, W. (2011). Analyzing Text Data for Opinion Mining. In: Muñoz, R., Montoyo, A., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2011. Lecture Notes in Computer Science, vol 6716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22327-3_49

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  • DOI: https://doi.org/10.1007/978-3-642-22327-3_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22326-6

  • Online ISBN: 978-3-642-22327-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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