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Reviewing Classification Approaches in Sentiment Analysis

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Soft Computing in Data Science (SCDS 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 545))

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Abstract

The advancement of web technologies has changed the way people share and express their opinions. People enthusiastically shared their thoughts and opinions via online media such as forums, blogs and social networks. The overwhelmed of online opinionated data have gained much attention by researchers especially in the field of text mining and natural language processing (NLP) to study in depth about sentiment analysis. There are several methods in classifying sentiment, including lexicon-based approach and machine learning approach. Each approach has its own advantages and disadvantages. However, there are not many literatures deliberate on the comparison of both approaches. This paper presents an overview of classification approaches in sentiment analysis. Various advantages and limitations of the sentiment classification approaches based on several criteria such as domain, classification type and accuracy are also discussed in this paper.

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Author information

Authors and Affiliations

  1. Faculty of Computer and Mathematical Sciences, University Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia

    Nor Nadiah Yusof, Azlinah Mohamed & Shuzlina Abdul-Rahman

Authors
  1. Nor Nadiah Yusof
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  2. Azlinah Mohamed
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  3. Shuzlina Abdul-Rahman
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Corresponding author

Correspondence to Nor Nadiah Yusof .

Editor information

Editors and Affiliations

  1. University of Tennessee, Knoxville, Tennessee, USA

    Michael W. Berry

  2. Universiti Teknologi MARA, Shah Alam, Malaysia

    Azlinah Mohamed

  3. Universiti Teknologi MARA, Shah Alam, Malaysia

    Bee Wah Yap

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Yusof, N.N., Mohamed, A., Abdul-Rahman, S. (2015). Reviewing Classification Approaches in Sentiment Analysis. In: Berry, M., Mohamed, A., Yap, B. (eds) Soft Computing in Data Science. SCDS 2015. Communications in Computer and Information Science, vol 545. Springer, Singapore. https://doi.org/10.1007/978-981-287-936-3_5

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  • DOI: https://doi.org/10.1007/978-981-287-936-3_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-935-6

  • Online ISBN: 978-981-287-936-3

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