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.
Fortune, N (2021). Text Mining Of Twitter Data: Topic Modelling. Afribary. Retrieved from https://tracking.afribary.com/works/text-mining-of-twitter-data-topic-modelling
Fortune, Njoku "Text Mining Of Twitter Data: Topic Modelling" Afribary. Afribary, 13 Apr. 2021, https://tracking.afribary.com/works/text-mining-of-twitter-data-topic-modelling. Accessed 24 Nov. 2024.
Fortune, Njoku . "Text Mining Of Twitter Data: Topic Modelling". Afribary, Afribary, 13 Apr. 2021. Web. 24 Nov. 2024. < https://tracking.afribary.com/works/text-mining-of-twitter-data-topic-modelling >.
Fortune, Njoku . "Text Mining Of Twitter Data: Topic Modelling" Afribary (2021). Accessed November 24, 2024. https://tracking.afribary.com/works/text-mining-of-twitter-data-topic-modelling