Twitter Event Summarization Using Phrase Reinforcement Algorithm and NLP Features

Abstract: 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. Twitter allows large no. of users to share/post their views, ideas on any particular event. According to recent survey, daily 340 million Tweets are sent on Twitter which is on a different topic and only 4% of posts on Twitter have relevant news data. It is not possible for any human to read the posts to get meaningful information related to specific events. There is one solution to this problem, i.e. we have to apply Summarization technique on it. In this paper, we have used an algorithm which uses a frequency count technique along with this we have also used some NLP features to summarize the event specified by the user. This automatic summarization algorithm handles the numerous, short, dissimilar, and noisy nature of tweets. We believe our novel approach helps users as well as researchers.

Keywords: Phrase Reinforcement Algorithm (PRA), Twitter API, Twitter, Natural Language Processing (NLP), Textual Entailment, Word Sense Disambiguation, WordNet, ROUGE Toolkit.