o:2315 Improving sentiment analysis for Twitter data by handling negation rules in the Serbian language en 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. : Sentiment Analysis, Serbian Language, Twitter, Negation Detection, Negation Rules, Machine Learning 1552099 10.2298/CSIS180122013L 1552101 1820-0214 2023-06-15T09:39:19.090Z 44 yes 46 Adela Ljajić State University of Novi Pazar 0000-0001-7326-059X Ulfeta Marovac State University of Novi Pazar person 0000-0001-7232-3755 application/pdf 383111 https://unilib.phaidrabg.rs/o:2315 no yes 1 70 92000001 71A08 Improving sentiment analysis for twitter data by handling negation rules in the Serbian language 16 16 1 289 311 The ComSIS Consortium 2019