A Novel Integration of Exploratory Data Analysis and LDA Text Mining for Deeper Understanding of Libyan-Related Tweets
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Text Mining, Topic Modeling, Latent Dirichlet Allocation Clustering, Twitter
Abstract
Identifying topics across digital sources such as scientific articles, web publications, and social media platforms is essential for summarising large text datasets and understanding emerging trends. Social networking platforms, in particular, generate vast amounts of data, creating an urgent need for topic extraction to uncover underlying themes. This study aims to extract topics from Arabic-language tweets posted by Libyan users on Twitter using a structured multi-stage approach. The study follows a four-stage methodology: (1) Data Collection, involving the construction of a Twitter corpus based on Libyan dialect-specific keywords; (2) Exploratory Analysis, which employs statistical analysis to identify key patterns in the data; (3) Preprocessing, which applies natural language processing techniques to prepare the dataset; and (4) Topic Modelling, where Latent Dirichlet Allocation (LDA) is applied to extract dominant topics. The exploratory analysis revealed behavioural patterns such as a high frequency of positive emoji usage, peak Twitter activity at 9 p.m., and the dominance of Android as the operating system. The topic modelling results identified major themes including Friendship, Travel, Political Issues, and Education. These findings provide insights into the interests and communication patterns of Libyan Twitter users and demonstrate the effectiveness of topic modelling in capturing culturally and regionally relevant discourse in social media data.
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