11 July 2016 Sentiment analysis of Arabic tweets using text mining techniques
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Proceedings Volume 10011, First International Workshop on Pattern Recognition; 100111F (2016) https://doi.org/10.1117/12.2242187
Event: First International Workshop on Pattern Recognition, 2016, Tokyo, Japan
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.
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Lamia Al-Horaibi, Lamia Al-Horaibi, Muhammad Badruddin Khan, Muhammad Badruddin Khan, } "Sentiment analysis of Arabic tweets using text mining techniques", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111F (11 July 2016); doi: 10.1117/12.2242187; https://doi.org/10.1117/12.2242187
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