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Techniques for improving the labelling process of sentiment analysis in the Saudi stock market

  • University of Bedfordshire
    ,
  • King Abdulaziz University
Research Output: Contribution to journal Article Peer-review

Open access

Abstract

Sentiment analysis is utilised to assess users' feedback and comments. Recently, researchers have shown an increased interest in this topic due to the spread and expansion of social networks. Users' feedback and comments are written in unstructured formats, usually with informal language, which presents challenges for sentiment analysis. For the Arabic language, further challenges exist due to the complexity of the language and no sentiment lexicon is available. Therefore, labelling carried out by hand can lead to mislabelling and misclassification. Consequently, inaccurate classification creates the need to construct a relabelling process for Arabic documents to remove noise in labelling. The aim of this study is to improve the labelling process of the sentiment analysis. Two approaches were utilised. First, a neutral class was added to create a framework of reliable Twitter tweets with positive, negative, or neutral sentiments. The second approach was improving the labelling process by relabelling. In this study, the relabelling process applied to only seven random features (positive or negative): "earnings" (Arabic source), "losses" (Arabic source), "green colour" (Arabic source:Arabic source), "growing" (Arabic source), "distribution" (Arabic source), "decrease" (Arabic source), "financial penalty" (Arabic source), and "delay" (Arabic source). Of the 48 tweets documented and examined, 20 tweets were relabelled and the classification error was reduced by 1.34%.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 34-43 (10 pages)

Journal (Volume, Issue Number)

International Journal of Advanced Computer Science and Applications (Volume 9, Issue 3)

Publication milestones

  • Published - 31/12/2018

Publication status

Published - 31/12/2018

ISSN

2158-107X

External Publication IDs

  • handle.net: 10547/623813
  • Scopus: 85049497212

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