Abstract
With the growth of online social network 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.
Highlights
People nowadays tend to rely heavily on the internet in their daily social and commercial activities
We investigate select topic modeling (TM) methods that are commonly used in text mining, namely, latent Dirichlet allocation (LDA), latent semantic analysis (LSA), non-negative matrix factorization (NMF), principal component analysis (PCA), and random projection (RP)
online social networks (OSNs) include a huge amount of user-generated content (UGC) with many irrelevant and noisy data, such as non-meaningful, inappropriate data and symbols that need to be filtered before applying any text analysis techniques
Summary
People nowadays tend to rely heavily on the internet in their daily social and commercial activities. There is a need for more efficient methods and tools that can aid in detecting and analyzing content in online social networks (OSNs), for those using user-generated content (UGC) as a source of data. There is a need to extract more useful and hidden information from numerous online sources that are stored as text and written in natural language within the social network landscape (e.g., Twitter, LinkedIn, and Facebook)
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