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.

Given a text data set; usually a collection of documents, one common task is to derive the topics in that data. Topic modelling is the process of applying statistical models (topic models) to extract the hidden / latent topics in the data. These models work by getting the hidden patterns in the document collection. Existing topic models include:

• Latent Semantic Analysis (LSA)
• Probabilistic Latent Semantic Analysis (PLSA)
• Latent Dirichlet Allocation (LDA)
• Correlated Topic Model (CTM)
• Explicit semantic analysis
• Hierarchical Dirichlet process
• Non-negative matrix factorization

The usefulness of Twitter data has only been scratched on the surface so far. This is because we can get information on nearly every topic of interest from Twitter; from entertainment to politics and even religion. Since Twitter has been available for the past thirteen years, it contains historical data on different topics and hence can be used for a track historic trends in different fields. In this work, we will be mining Nigerian Health Twitter data for the past five years to derive different topic and possible trends using topic modelling.

• 2015: Ebola outbreak

• 2016: Lasser fever outbreak

• 2017: Meningitis outbreak

• 2018: Child immunization

Njoku, Uchechukwu Fortune. Text Mining of Twitter Data: Topic Modelling. Master's Thesis. 2019.

Document link.

Colab notebook.