Discovering conversational topics and emotions associated with Demonetization tweets in India
This provides insights into public discourse on a specific economic policy in India, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of analyzing public sentiment and discussion topics on Twitter regarding India's demonetization event, using LDA-based topic modeling and emotion analysis to extract latent topics and opinions, with results validated via Normalized Mutual Information (NMI).
Social media platforms contain great wealth of information which provides us opportunities explore hidden patterns or unknown correlations, and understand people's satisfaction with what they are discussing. As one showcase, in this paper, we summarize the data set 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.