SENTIMENTANALYSIS FOR ARABIC AND ENGLISH DATASETS | ||||
| International Journal of Intelligent Computing and Information Sciences | ||||
| Article 5, Volume 15, Issue 1, January 2015, Page 55-70 PDF (324.57 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijicis.2015.10911 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| R Elawady1; S Barakat2; N Elrashidy2 | ||||
| 1Communication and Computer systems Department, Faculty of Engineering, Mansoura University - Egypt | ||||
| 2Information Systems Department, Faculty of Computers and Information,Mansoura University - Egypt | ||||
| Abstract | ||||
| Sentiment analysis is an important topic that has tracked attention since 2001. It basically is text 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 widespread availability of opinionated posts, making research in the area more viable and important. We need to make sentiment analysis to calculate the percentage of user acceptance or rejection according to their comments.Although Arabic is the native language of hundreds of millions of people in twenty countries across the Middle East and North Africa, the research in the area of Arabic sentiment analysis is progressing at a very slow pace compared to that being carried out in English[2].In this paper, we presnet 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 from YouTubeArabic pages. We applied more than one machine learning on algorithms both (Arabic. English) (Decision trees, Navie Bayes, functions, and support vector machines. We also createda Sentiword Lexicon based on the Corpus that we gathered. Then we evaluated each method and compared their accuracies. | ||||
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