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Developing Evaluation Model of Topical Term for Document-Level Sentiment Classification

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 5351)

Abstract

Sentiment classification is used to identify whether the opinion expressed in a document is positive or negative. In this paper, we present an evaluation modeling approach to document-level sentiment classification. The motivation of this work stems from the observation that the global document classification can benefit greatly by learning how a topical term is evaluated in its local sentence context. Two sentence-level sentiment evaluation models, namely positive and negative models, are constructed for each topical term. When analyzing a document, the evaluation models generate divergence to support sentence classification that in turn can be used to decide on the whole document classification collectively. When evaluated on a public available movie review corpus, the experimental results are comparable with the ones published. This is quite encouraging to us and motivates us to further investigate how to develop more effective evaluation models in the future.

Keywords

  • Sentiment Classification
  • Evaluation Model
  • Topical Term
  • Maximum Spanning Tree

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© 2008 Springer-Verlag Berlin Heidelberg

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Hu, Y., Li, W., Lu, Q. (2008). Developing Evaluation Model of Topical Term for Document-Level Sentiment Classification. In: Ho, TB., Zhou, ZH. (eds) PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008. Lecture Notes in Computer Science(), vol 5351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89197-0_19

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  • DOI: https://doi.org/10.1007/978-3-540-89197-0_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89196-3

  • Online ISBN: 978-3-540-89197-0

  • eBook Packages: Computer ScienceComputer Science (R0)