Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis - Albalawi, Rania; Yeap, Tet Hin; Benyoucef, Morad (Frontiers in Artificial Intelligence, 2020)
With the growth of online social networks platforms and applications, large amounts of textual user-generated content are created daily in the form of comments, reviews, and short text messages. As a result, users often find it challenging to discover useful information or more on the topic being discussed from such content. Machine learning and natural language processing algorithms are used to analyze the massive amount of textual social media data available online, including topic modeling techniques that have gained popularity in recent years. This paper investigates the topic modeling subject and its common application areas, methods and tools. Also, we examine and compare five frequently used topic modeling methods, as applied to short textual social data, to show their benefits practically in detecting important topics. These methods are Latent Semantic Analysis, Latent Dirichlet Allocation, Non-negative Matrix Factorization, Random Projection, and Principal Component Analysis. Two textual datasets were selected to evaluate the performance of included topic modeling methods based on the topic quality and some standard statistical evaluation metrics, like recall, precision, F-score and topic coherence. As a result, Latent Dirichlet Allocation and Non-negative Matrix Factorization methods delivered more meaningful extracted topics and obtained good results. The paper sheds light on some common topic modeling methods in a short text context and provides direction for researchers who seek to apply these methods.