SENTIMENTANALYSIS FOR ARABIC AND ENGLISH DATASETS
R. El-Awady, Sherif Barakat, Nora Mahmoud El-Rashidy · International journal of intelligent computing and information sciences/International Journal of Intelligent Computing and Information Sciences · 2015
Sentiment analysis is an important topic that has tracked attention since 2001. It basically istext classification based on analyzing opinions that expressed by writing (e.g., social media, blogs,discussion groups, etc). The widespread use of social networks has, also, led to a widespreadavailability of opinionated posts, making research in the area more viable and important. We need tomake sentiment analysis to calculate the percentage of user acceptance or rejection according to theircomments.Although Arabic is the native language of hundreds of millions of people in twenty countriesacross the Middle East and North Africa, the research in the area of Arabic sentiment analysis isprogressing at a very slow pace compared to that being carried out in English[2].In this paper, wepresnet our work in which we start by testing on English texts that wrere collected from Amazon (book,DVD, and electronics).Then, we applied the same process on Arabic dataset that we collect fromYouTubeArabic pages. We applied more than one machine learning on algorithms both (Arabic.English) (Decision trees, Navie Bayes, functions, and support vector machines. We also createdaSentiword Lexicon based on the Corpus that we gathered. Then we evaluated each method andcompared their accuracies.