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Modelling Public Sentiment in Twitter: Using Linguistic Patterns to Enhance Supervised Learning

  • Conference paper
Computational Linguistics and Intelligent Text Processing (CICLing 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9042))

Abstract

This paper describes a Twitter sentiment analysis system that classifies a tweet as positive or negative based on its overall tweet-level polarity. Supervised learning classifiers often misclassify tweets containing conjunctions such as “but” and conditionals such as “if”, due to their special linguistic characteristics. These classifiers also assign a decision score very close to the decision boundary for a large number tweets, which suggests that they are simply unsure instead of being completely wrong about these tweets. To counter these two challenges, this paper proposes a system that enhances supervised learning for polarity classification by leveraging on linguistic rules and sentic computing resources. The proposed method is evaluated on two publicly available Twitter corpora to illustrate its effectiveness.

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Author information

Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Prerna Chikersal, Soujanya Poria, Erik Cambria & Chng Eng Siong

  2. Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico, Mexico

    Alexander Gelbukh

Authors
  1. Prerna Chikersal
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  2. Soujanya Poria
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  3. Erik Cambria
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  4. Alexander Gelbukh
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  5. Chng Eng Siong
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Corresponding author

Correspondence to Prerna Chikersal .

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Editors and Affiliations

  1. Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico DF, Mexico

    Alexander Gelbukh

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Chikersal, P., Poria, S., Cambria, E., Gelbukh, A., Siong, C.E. (2015). Modelling Public Sentiment in Twitter: Using Linguistic Patterns to Enhance Supervised Learning. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-18117-2_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18116-5

  • Online ISBN: 978-3-319-18117-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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