Adaptive cumulative voting-based aggregation algorithm for combining multiple clusterings of chemical structures

Abstract

Social media such as Twitter and Facebook can be considered as a new media different from the typical media group. The information on social media spread much faster than any other traditional news media due to the fact that people can upload information with no constrain to time or location. People also express their emotional status to let others know what they feel about information. For this reason many studies have been testing social media data to uncover hidden information under textual sentences. Analyzing social media is not simple due to the huge volume and variety of data. Many researches dealt with limited domain area to overcome the size issue. This study focuses on how the flow of sentiments and frequency of tweets are changed from November to December in 2009. We analyzed 110 million tweets collected by Stanford University and LIWC (Linguistic Inquir y Word Count) for sentiment analysis. We did find that people were not happy in afternoon but they were happy in night time as many psychologists suggested before. After analyzing large volume of tweets, we were also able to find the precise day when breaking events occurred. This study offer dive rse evidence to prove that Twitter has valuable information for tracking breaking news over the world.