Improving Sentiment Analysis for Twitter Data by Handling Negation Rules in the Serbian Language

Adela Ljajić1 and Ulfeta Marovac1

  1. State University of Novi Pazar
    Vuka Karadžića bb, 36300 Novi Pazar, Serbia
    {acrnisanin, umarovac}@np.ac.rs

Abstract

The importance of determining sentiment for short text increases with the rise in the number of comments on social networks. The presence of negation in these texts affects their sentiment, because it has a greater range of action in proportion to the length of the text. In this paper, we examine how the treatment of negation impacts the sentiment of tweets in the Serbian language. The grammatical rules that influence the change of polarity are processed. We performed an analysis of the effect of the negation treatment on the overall process of sentiment analysis. A statistically significant relative improvement was obtained (up to 31.16% or up to 2.65%) when the negation was processed using our rules with the lexicon-based approach or machine learning methods. By applying machine learning methods, an accuracy of 68.84% was achieved on a set of positive, negative and neutral tweets, and an accuracy of as much as 91.13% when applied to the set of positive and negative tweets.

Key words

Sentiment Analysis, Serbian Language, Twitter, Negation Detection, Negation Rules, Machine Learning

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS180122013L

Publication information

Volume 16, Issue 1 (January 2019)
Year of Publication: 2019
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Ljajić, A., Marovac, U.: Improving Sentiment Analysis for Twitter Data by Handling Negation Rules in the Serbian Language. Computer Science and Information Systems, Vol. 16, No. 1, 289-311. (2019), https://doi.org/10.2298/CSIS180122013L