Text Mining of Twitter Data: Topic Modelling

Abstract

Access to the Internet is becoming more affordable especially in Africa and with this the number of active social media users is also on the rise. Twitter is a social media platform on which users post and interact with messages known as "tweets". These tweets are usually short with a limit of 280 characters. With over 100 million Internet users and 6 million active monthly users in Nigeria, lots of data is generated through this medium daily. This thesis aims to gain insights from the ever-growing Nigerian data generated from twitter using Topic modelling. We use Latent Dirichlet Allocation (LDA) on Nigerian heath tweets from verified accounts covering time period of 2015 – 2019 to derive top health topics in Nigeria. We detected the outbreaks of Ebola, Lassa fever and meningitis within this time frame. We also detected reoccurring topics of child immunization/vaccination. Twitter data contains useful information that can give insights to individuals, organizations and the government hence it should be further explored and utilized.