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Title: SMS Sentiment Classification based on Lexical Features, Emoticons and Informal Abbreviations
Authors: Šandrih, Branislava
Keywords: Computer Application in Arts and Humanities
Web-Based Services
Document Analysis
Issue Date: 2019
Publisher: Institute of Mathematics and Informatics Bulgarian Academy of Sciences
Citation: Serdica Journal of Computing, Vol. 13, No 1-2, (2019), 081p-096p
Abstract: In this paper we investigate the influence of emoticons, informal speech, lexical and other linguistic features on the sentiment contained in SMS messages. Using the dataset of ∼ 6,000 samples, we trained a linear SVM classifier able to determine positive, negative and neutral sentiments. The dataset mostly contains messages in Serbian, but also in English and German. The classifier had an average accuracy score of 92.3% in a 5-fold Cross Validation setting, and F1-score of 92.1%, 74.0% and 93.3% in favor of the positive, negative and neutral class, respectively.
ISSN: 1312-6555
Appears in Collections:Volume 13, Number 1-2

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