2017/05/23 by Zhengkui Wang, Wang, Zhengkui, Guangdong Bai +7 · 1 citation
Computer Science · Physics and Astronomy · #Advanced Text Analysis Techniques #Complex Network Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Sentiment Analysis and Opinion Mining #cs.CL #cs.IR
paper · pdf · doi:10.48550/arxiv.1705.08094
arxiv created 2017/05/23 · openalex publication_date 2017/05/23 · arxiv updated 2017/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.