Personalization of Trending Tweets using Like-dislike Category Model

Twitter is a popular social media platform, where millions of tweets are being generated every day. On Twitter, users can tweet about certain topic during occurrence of events. This results in trending topics, such as #MH370, #MH17, #South Korea ferry etc. When viewing trending topic, not all the tweets are relevant to one self. Therefore, it is important to classify these tweets based on individual preference for better information retrieval. To address this problem, this paper focuses on automated personalization of tweets for popular trending topics. The main objective is to classify the tweets information as “Like” or “Dislike” on a particular topic depending on personal preference by feature selection. For enhancement, topic-related keywords are selected as features for representing a category model from user preferred tweets and other sources like news. Finally, the result of experiment shows promising outcome that a training based on low number of tweets can give quick personalization on next incoming tweets.