TwiInsight: Discovering Topics and Sentiments from Social Media Datasets
This work addresses the need for automated social media analysis for researchers or analysts, but it is incremental as it combines existing methods without major innovations.
The paper tackles the problem of extracting topics and sentiments from Twitter data by presenting TwiInsight, a system that automatically identifies popular topics across categories and analyzes opinions, with results including the comparison of six algorithms for sentiment analysis and topic modeling.
Social media platforms contain a great wealth of information which provides opportunities for us to explore hidden patterns or unknown correlations, and understand people's satisfaction with what they are discussing. As one showcase, in this paper, we present a system, TwiInsight which explores the insight of Twitter data. Different from other Twitter analysis systems, TwiInsight automatically extracts the popular topics under different categories (e.g., healthcare, food, technology, sports and transport) discussed in Twitter via topic modeling and also identifies the correlated topics across different categories. Additionally, it also discovers the people's opinions on the tweets and topics via the sentiment analysis. The system also employs an intuitive and informative visualization to show the uncovered insight. Furthermore, we also develop and compare six most popular algorithms - three for sentiment analysis and three for topic modeling.