Topic Modeling and Sentiment Analysis of Twitter Discussions on COVID-19 from Spatial and Temporal Perspectives

The study reported in this paper aimed to evaluate the topics and opinions of COVID-19 discussion found on Twitter It performed topic modeling and sentiment analysis of tweets posted during the COVID-19 outbreak, and compared these results over space and time In addition, by covering a more recent and a longer period of the pandemic timeline, several patterns not previously reported in the literature were revealed Author-pooled Latent Dirichlet Allocation (LDA) was used to generate twenty topics that discuss different aspects related to the pandemic Time-series analysis of the distribution of tweets over topics was performed to explore how the discussion on each topic changed over time, and the potential reasons behind the change In addition, spatial analysis of topics was performed by comparing the percentage of tweets in each topic among top tweeting countries Afterward, sentiment analysis of tweets was performed at both temporal and spatial levels Our intention was to analyze how the sentiment differs between countries and in response to certain events The performance of the topic model was assessed by being compared with other alternative topic modeling techniques The topic coherence was measured for the different techniques while changing the number of topics Results showed that the pooling by author before performing LDA significantly improved the produced topic models © Iyad AlAgha, 2021