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Proceedings Paper

Sentiment analysis of Arabic tweets using text mining techniques
Author(s): Lamia Al-Horaibi; Muhammad Badruddin Khan
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Paper Abstract

Sentiment analysis has become a flourishing field of text mining and natural language processing. Sentiment analysis aims to determine whether the text is written to express positive, negative, or neutral emotions about a certain domain. Most sentiment analysis researchers focus on English texts, with very limited resources available for other complex languages, such as Arabic. In this study, the target was to develop an initial model that performs satisfactorily and measures Arabic Twitter sentiment by using machine learning approach, Naïve Bayes and Decision Tree for classification algorithms. The datasets used contains more than 2,000 Arabic tweets collected from Twitter. We performed several experiments to check the performance of the two algorithms classifiers using different combinations of text-processing functions. We found that available facilities for Arabic text processing need to be made from scratch or improved to develop accurate classifiers. The small functionalities developed by us in a Python language environment helped improve the results and proved that sentiment analysis in the Arabic domain needs lot of work on the lexicon side.

Paper Details

Date Published: 11 July 2016
PDF: 5 pages
Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111F (11 July 2016); doi: 10.1117/12.2242187
Show Author Affiliations
Lamia Al-Horaibi, Al Imam Mohammad Ibn Saud Islamic Univ. (Saudi Arabia)
Muhammad Badruddin Khan, Al Imam Mohammad Ibn Saud Islamic Univ. (Saudi Arabia)


Published in SPIE Proceedings Vol. 10011:
First International Workshop on Pattern Recognition
Xudong Jiang; Guojian Chen; Genci Capi; Chiharu Ishll, Editor(s)

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