Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis

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.

A comparative analysis of Latent Semantic analysis and Latent Dirichlet allocation topic modeling methods using Bible data

“…However, the studies conducted by Siti Qomariyaha et al in 2019 (26) by using Twitter data as text data were corroborated with our results in this study as they concluded that LDA considers the relationship between documents in the corpus with the best topic coherence than LSA. Also, in comparative studies using different text mining methods as applied to short text data, LDA showed more meaningful extracted topics and obtained good results with topic coherence as an evaluation metric for creating the content of a document collection (6,27) .…”

“…Latent Semantic Analysis is a method for representing and extracting the contextual meaning of words through statistical computations over a text corpus (6) , (14,15) . It is formerly known as Latent Semantic Indexing (LSI) (16) , before LSI, Information is fetched by accurately matching words in documents with the queries using lexical matching methods.…”

“…The topic models allow in analyzing a set of documents based on statistics of words in each, to express what the topic might be and what each document's balance of topics. It also refers to a probabilistic topic model to use statistical algorithms for discovering hidden topics of the collection of documents (4)(5)(6) . The significant and crucial step in the accuracy and storage of the information is quality management and extraction according to the information that is present.…”

Deneysel Yazılım Mühendisliğindeki Araştırma Eğilimleri için Metin Madenciliği

“…However, the algorithm cannot model the relationships between the created topics. LDA is the most popular algorithm which is studied extensively in many domains [21] and reported to work effectively in generating the context for the collection of documents [22]. [23] overviews topic modeling approaches and suggest a guideline for choosing most suitable method for the specific analysis.…”

Enabling Efficient and Scalable Service Search in IoT With Topic Modeling: An Evaluation

“…However, research efforts [9], [17] is limited in this field, and they are based on Latent Dirichlet Allocation (LDA) [13], which does not perform well in short texts because of their sparseness. Recently, many TM-based short texts (e.g., tweets, or Facebook status) categorisation approaches [15], [16], [19]- [23], have proposed novel methods to improve the performance of topic-based categorisers in short texts. These proposals can be useful in IoT service descriptions categorisation as they are generally short and sparse.…”