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Social media platforms contain a great wealth of information which providesopportunities for us to explore hidden patterns or unknown correlations, andunderstand people's satisfaction with what they are discussing. As oneshowcase, in this paper, we present a system, TwiInsight which explores theinsight 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 Twittervia topic modeling and also identifies the correlated topics across differentcategories. Additionally, it also discovers the people's opinions on the tweetsand topics via the sentiment analysis. The system also employs an intuitive andinformative visualization to show the uncovered insight. Furthermore, we alsodevelop and compare six most popular algorithms - three for sentiment analysisand three for topic modeling.

TwiInsight: Discovering Topics and Sentiments from Social Media Datasets