Opinions Mining in Twitter

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.

Mining Public Opinion about Economic Issues: Twitter and the U.S. Presidential Election

Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This paper proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election