We bring the data from the social networking site Twitter
pages, and then we have worked on cleaning and processing
operation to the text of for the classification process texts retrieved
contain a lot of noise and information is useful for the pr
ocess of
analyzing the views, such as advertisements and links and e-mail
addresses and the presence of many words that do not affect the
general orientation of the text, and then get all the publications in
the Twitter page and what are the comments about each tweets is
intended to know the proportion of supporters and opponents of
this publication.
We apply Naïve Bayes algorithm in classification, we had the
appropriate training, and after passing Posts and comments data
(opinions), we got good results on the ratio of supporters of the
post and the percentage of his opponents.
Arabic sentiment analysis research existing currently is very limited. While sentiment analysis has many applications in English, the Arabic language is still recognizing its early steps in this field. In this paper, we show an application
on Arabic
sentiment analysis by implementing a sentiment classification for Arabic tweets. The retrieved tweets are analyzed to provide their sentiments polarity (positive, or negative). Since, this data is collected from the social network Twitter; it has its importance for the Middle East region, which mostly speaks Arabic
