Summaration Generating Timeline for Evolutionary Tweet Streams and Categorizing Tweets
International Journal for Research in Applied Science and Engineering Technology
Now a day's social networking sites are the fastest medium which delivers news to the user as compared to the newspaper and television. There so many social networking sites are present and one of them is Twitter. People run-out of time and even then, they find a small time to know what's happening all over the world. So, in such hefty schedule people can't go through all the tweets which people post all day long and the model proposed is summarizing the tweets and generating timeline for
... timeline for evolutionary tweet streams. It consists three components, first tweet stream clustering for clustering tweets using Bisect kmeans cluster algorithm, so that the tweets which are taken from the twitter Application Programming Interface (API) are preprocessed and then cluster. Second component tweet summarization cluster vector technique for generating rank summarization using LexRank algorithm, third component is to categorize tweets into positive, negative and neutral tweets using sentiment analysis where tweets are collected by using hash tags (screen names) of twitter individual account so that it will display the positive, negative ,neutral tweets of that individual account. Doing categorization tweets for the particular user then it will display the positive tweets percentage:22.2%, negative tweets percentage 11.11%, neutral tweets of 66.6% and the results are plotted on bar and pie chat.