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Temporal analysis of topic modeling output by machine learning techniques
Topic modeling is widely recognized as one of the most effective and significant methods of unsupervised text analysis. This method facilitates...
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Topic Modeling for Mining Opinion Aspects from a Customer Feedback Corpus
AbstractThe paper introduces a methodology for extracting opinion aspects from textual content by identifying the customer-evaluated parameters...
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An integrated clustering and BERT framework for improved topic modeling
Topic modelling is a machine learning technique that is extensively used in Natural Language Processing (NLP) applications to infer topics within...
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Hybrid topic modeling method based on dirichlet multinomial mixture and fuzzy match algorithm for short text clustering
Topic modeling methods proved to be effective for inferring latent topics from short texts. Dealing with short texts is challenging yet helpful for...
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An optimized topic modeling question answering system for web-based questions
The ability of the system to answer the searched formal queries has become active research in recent times. However, for the wide range of data, the...
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An Approach for Analyzing Unstructured Text Data Using Topic Modeling Techniques for Efficient Information Extraction
Topic modeling techniques are popularly used for document clustering, large-scale text analysis, information extraction from unstructured text...
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Topic Modeling Applied to Reddit Posts
Text data is widely used for both commercial and research purposes. While extensive sources of text data are available within Internet forums, such...
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A decadal study on identifying latent topics and research trends in open access LIS journals using topic modeling approach
The study utilized Latent Dirichlet Allocation (LDA) Topic modeling to identify prevalent latent topics within Open Access (OA) Library and...
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Mining technology trends in scientific publications: a graph propagated neural topic modeling approach
The past decades have witnessed significant progress in scientific research, where new technologies emerge and traditional technologies constantly...
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Topic Modeling
Topic modeling is usually used to identify the hidden theme/concept using an algorithm based on high word frequency among the documents. It can be...
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Text Summarization and Topic Modeling
This chapter covers text summarization and topic modelling. Text summarization and topic modelling have become critically important, especially for...
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Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics
Advancements in artificial intelligence (AI) have driven extensive research into develo** diverse multimodal data analysis approaches for smart...
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Emerging topic identification from app reviews via adaptive online biterm topic modeling
Emerging topics in app reviews highlight the topics (e.g., software bugs) with which users are concerned during certain periods. Identifying emerging...
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An Attention Hierarchical Topic Modeling
AbstractProbabilistic topic models have been used to detect topic-based content presentations when facing a collection of documents. However, topic...
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Effective Implementations of Topic Modeling Algorithms
AbstractIn this paper, we provide an overview of effective EM-like learning algorithms for latent Dirichlet allocation (LDA) models and additively...
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An exploratory study of net zero discourse based on South Korean newspapers: a topic modeling and sentiment analysis approach
Public support for net zero is an important determinant of the solution for climate change. Newspapers can be used as a data source for observing...
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Green and sustainable AI research: an integrated thematic and topic modeling analysis
This investigation delves into Green AI and Sustainable AI literature through a dual-analytical approach, combining thematic analysis with BERTopic...
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Empirical research of emerging trends and patterns across the flipped classroom studies using topic modeling
This study presents topic modeling based bibliometric characteristics of the articles related to the flipped classroom. The corpus of the study...
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Semantic similarity measure for topic modeling using latent Dirichlet allocation and collapsed Gibbs sampling
Automatically extracting topics from large amounts of text is one of the main uses of natural language processing (NLP). The latent Dirichlet...
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Topic modeling in software engineering research
Topic modeling using models such as Latent Dirichlet Allocation (LDA) is a text mining technique to extract human-readable semantic “topics” (i.e.,...
