Discovering conversational topics and emotions associated with Demonetization tweets in India

TL;DR

This paper analyzes Twitter data related to India's demonetization, automatically extracting latent topics and emotions to uncover public sentiment and discussion patterns using LDA and emotion analysis.

Contribution

It introduces an automated system combining LDA topic modeling and emotion analysis to explore and visualize public opinions on demonetization from Twitter data.

Findings

Effective extraction of discussion topics

Identification of correlated topics across categories

Emotion analysis reveals public sentiment

Abstract

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.