Topic Modelling and Event Identification from Twitter Textual Data
This provides a tool for researchers and practitioners to analyze large volumes of social media text, but it is incremental as it uses existing methods on new data.
The study applied Latent Dirichlet Allocation (LDA) topic modeling to Twitter data from social events in Kenya, showing it effectively extracted and summarized discussion topics for manual analysis.
The tremendous growth of social media content on the Internet has inspired the development of the text analytics to understand and solve real-life problems. Leveraging statistical topic modelling helps researchers and practitioners in better comprehension of textual content as well as provides useful information for further analysis. Statistical topic modelling becomes especially important when we work with large volumes of dynamic text, e.g., Facebook or Twitter datasets. In this study, we summarize the message content of four data sets of Twitter messages relating to challenging social events in Kenya. We use Latent Dirichlet Allocation (LDA) topic modelling to analyze the content. Our study uses two evaluation measures, Normalized Mutual Information (NMI) and topic coherence analysis, 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