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Topic based document modeling for information filtering
CTU Journal of Innovation and Sustainable Development, 2023 Information Filtering (IF), which has been popularly studied in recent years, is one of the areas that applies document retrieval techniques for dealing with the huge amount of information. In IF systems, modelling user’s interest and filtering relevant
Tran Diem Hanh Nguyen
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Introducción a Topic Modeling y MALLET
The Programming Historian en Español, 2018 Esta lección explica qué es topic modeling y por qué podrías querer utilizarlo en tus investigaciones. Luego aprenderás cómo instalar y trabajar con MALLET, una caja de herramientas para procesamiento de lenguajes naturales (PLN) con la que topic ...
Shawn Graham +2 more
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LDA-Based Topic Modeling Sentiment Analysis Using Topic/Document/Sentence (TDS) Model
Applied Sciences, 2021 Customer reviews on the Internet reflect users’ sentiments about the product, service, and social events. As sentiments can be divided into positive, negative, and neutral forms, sentiment analysis processes identify the polarity of information in the ...
Akhmedov Farkhod +3 more
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Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews
JOIN: Jurnal Online Informatika, 2023 Annotation and analysis of online customer reviews were identified as significant problems in various domains, including business intelligence, marketing, and e-governance.
Valentinus Roby Hananto
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Evaluation of unsupervised static topic models’ emergence detection ability [PDF]
PeerJ Computer ScienceDetecting emerging topics is crucial for understanding research trends, technological advancements, and shifts in public discourse. While unsupervised topic modeling techniques such as Latent Dirichlet allocation (LDA), BERTopic, and CoWords clustering ...
Xue Li +5 more
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The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling
IEEE Access, 2023 In the era of big data and ubiquitous internet connectivity, user feedback data plays a crucial role in product development and improvement. However, extracting valuable insights from the vast pool of unstructured text data found in user feedback ...
Mohammad Hamid Asnawi +3 more
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Probabilistic Topic Models [PDF]
IEEE Signal Processing Magazine, 2010 In this article, we review probabilistic topic models: graphical models that can be used to summarize a large collection of documents with a smaller number of distributions over words. Those distributions are called "topics" because, when fit to data, they capture the salient themes that run through the collection.
David M. Blei +2 more
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CoRR, 2011 Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents' mixture weight beliefs are replaced with squashed Gaussian distributions.
Hennig, P. +3 more
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Marketing Insights from Reviews Using Topic Modeling with BERTopic and Deep Clustering Network
Applied Sciences, 2023 The feedback shared by consumers on e-commerce platforms holds immense value in marketing, as it offers insights into their opinions and preferences, which are readily accessible.
Yusung An, Hayoung Oh, Joosik Lee
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A Topic Coverage Approach to Evaluation of Topic Models [PDF]
IEEE Access, 2021 Topic models are widely used unsupervised models capable of learning topics - weighted lists of words and documents - from large collections of text documents. When topic models are used for discovery of topics in text collections, a question that arises naturally is how well the model-induced topics correspond to topics of interest to the analyst.
Damir Korencic +3 more
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