Ms. Lamia Al-Horaibi Profile

Lamia Al-Horaibi

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Proceedings Article | 11 July 2016 Paper

Sentiment analysis of Arabic tweets using text mining techniques

Lamia Al-Horaibi, Muhammad Badruddin Khan

Proceedings Volume 10011, 100111F (2016) https://doi.org/10.1117/12.2242187

KEYWORDS: Analytical research, Mining, Machine learning, Algorithm development, Detection and tracking algorithms, Feature extraction, Computer programming, Data analysis, Library classification systems, Computing systems

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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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