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 process 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.