IJMLC 2018 Vol.8(4): 399-403 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.4.719

A Review of Contextual Information for Context-Based Approach in Sentiment Analysis

Nor Nadiah Yusof, Azlinah Mohamed, and Shuzlina Abdul-Rahman

Abstract—Online opinionated data has increased tremendously since the arrival of web 2.0. Users have the authority to generate online content expressing their sentiments or opinions regarding subjects of interest. Although these phenomena caused the problem of information overload, the opinionated data is valuable and beneficial to others. Looking at the prominent significance of this issue, many researchers grab this opportunity to further investigate sentiment analysis. The main task of sentiment analysis is to classify opinions into several orientations. However, the orientation of sentiment is highly dependent on the context of sentiment text. Thus, it is important to consider contextual information in order to correctly classify sentiments. This paper would like to facilitate researchers in capturing the contextual information of context-based approach in sentiment analysis; which includes sequence and collocation of words, negation handling, and ambiguity. Several recent studies that utilize the contextual information are also discussed. Besides, this paper presents the state of art of context based on the level, type, and representation of context. With the help of this review, researchers will be able to correctly classify sentiments based on context in improving the performance of sentiment analyzer.

Index Terms—Contextual information, context-based approach, natural language processing, sentiment analysis.

Nor Nadiah Yusof, Azlinah Mohamed and Shuzlina Abdul Rahman are with Faculty of Computer & Mathematical Science, Universiti Teknologi MARA, Malaysia (e-mail: nornadiah.yusof@gmail.com, {azlinah, shuzlina}@tmsk.uitm.edu.my).


Cite: Nor Nadiah Yusof, Azlinah Mohamed, and Shuzlina Abdul-Rahman, "A Review of Contextual Information for Context-Based Approach in Sentiment Analysis," International Journal of Machine Learning and Computing vol. 8, no. 4, pp. 399-403, 2018.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net