Trending Topic Analysis using novel sub topic detection model

Abstract

Twitter collects millions of tweets every day which in turn serves as a rich information delivering platform. However, some users, especially new users, often find it difficult to understand trending topics in Twitter when confronted with overwhelming and unorganized tweets. Previously, there has been attempts to provide a short snippet to summarize a topic but, this does not scale up to user's expectation as it does not provide any analyzed summary. This work aims to develop a Trending Topic Analysis System to analyze trending topics by performing Topic based sentiment classification thereby summarizing public views on selected trending topics and to generate extractive sub summaries of topics over the time period using a novel sub topic detection approach. Different from the traditional summarization framework, conflicting summary generation could be avoided with sentiment classification enhanced by common and tweet specific feature extraction thereby sorting the data into separate sentiment corpus. Volume-based followed by topic modelled approach of detecting sub topic in the corpus help detect subtopics under the trending topic more efficiently. A new approach called Foreground Dynamic Topic Modelling (VF-DTM) is proposed which distills the noisy content and extracts the foreground tweets from the corpus and then build the intended model on it. Efficiency of Sub topic detection in turn increases the quality of the extractive sub summaries generated.