Uncovering Trending Stories from Twitter by Extracting Ground Truth from Datasets

Abstract

Social networking services like Twitter generates contents that reflects series of conversation which shows real-world events. Twitter is social networking site that provide service for a large number of users to communicate with each other simultaneously; it is an asymmetrical relationship between friends and followers that provides an interesting structure among the users of Twitter. Twitter’s series of messages called tweets, which are restricted to 140 characters and thus are usually much focused. The basic process is to capture tweets from twitter that extract mostly discussed topic in between users. This tweet dataset can be process for finding trending stories using standard natural language processing. An Uncovering trending stories is therefore a building block is to extract and summarizes the information raised from social networking services. There is verity of methods for finding trending stories that improves quality of result. This paper proposes application for uncovering trending topics from twitter datasets using BNgram topic detection method.
DOI: 10.17762/ijritcc2321-8169.150691