Abstract

Social media platforms, owing to its great wealth of information, facilitates one’s opportunities to explore hidden patterns or unknown correlations. It also finds its credibility in understanding people’s expressions from what they are discussing on online platforms. As one showcase, in this paper, we summarize the dataset of Twitter messages related to recent demonetization of all Rs. 500 and Rs. 1000 notes in India and explore insights from Twitter’s data. Our proposed system automatically extracts the popular latent topics in conversations regarding demonetization discussed in Twitter via the Latent Dirichlet Allocation (LDA)-based topic model and also identifies the correlated topics across different categories. Additionally, it also discovers people’s opinions expressed through their tweets related to the event under consideration via the emotion analyzer. The system also employs an intuitive and informative visualization to show the uncovered insight. Furthermore, we use an evaluation measure, Normalized Mutual Information (NMI), to select the best LDA models. The obtained LDA results show that the tool can be effectively used to extract discussion topics and summarize them for further manual analysis.

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Recommended citation: Niyogi, M., Kumar Pal, A. (2019). “Discovering Conversational Topics and Emotions Associated with Demonetization Tweets in India”. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, Singapore.