Topic Modeling: A Comprehensive Review

Kherwa, Pooja; Bansal, Poonam

doi:10.4108/eai.13-7-2018.159623

Cited by 142 publications

(127 citation statements)

References 28 publications

(34 reference statements)

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“…TM is a methodology for processing the massive volume of data generated in OSNs and extracting the veiled concepts, protruding features, and latent variables from data that depend on the context of the application (Kherwa and Bansal, 2018 ). Several methods can operate in the areas of information retrieval and text mining to perform keyword and topic extraction, such as MAUI, Gensim, and KEA.…”

Section: Proposed Topic Modeling Methodologymentioning

confidence: 99%

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

Albalawi

Yeap

Benyoucef

2020

Front. Artif. Intell.

229

130

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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.

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Section: Proposed Topic Modeling Methodologymentioning

confidence: 99%

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

Albalawi

Yeap

Benyoucef

2020

Front. Artif. Intell.

229

130

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“…Topic modeling is a technique where it aims to give the reader better understanding by identifying natural topics in the text. There are four most popular methods in topic modeling which are Latent Semantic Analysis (LSA), Non-Negative Matrix Factorization (NNMF), Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA) [18].…”

Section: Topic Modelingmentioning

confidence: 99%

Chatbot Developments in The Business World

Sari¹,

Virnilia²,

Susanto³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Chatbot is an application that is designed to help humans communicate with machines. NLP and API.AI are some of the components that are crucial in making chatbot as they help chatbot in performing chatting operations. Chatbot could be implemented in many aspects of life such as education, social media platforms, games, even in business. Business has always been an important part of human life and it is irreplaceable. As the era evolves, the business industry is also getting modernized and automated which resulted in implementing the latest technology into the industry like chatbot. In the context of the business industry, chatbot can be used as an assistant that will help dealing with the customer service section as well as giving the latest information about the business. This study aims to determine how the chatbot gives impact to the business as well as the owners, what kind of features that the chatbot needs, and how the chatbot should be developed so that it will be compatible in the business environment. The research was conducted using two methods which are literature review and survey: questionnaire. The result of the research conducted are the impacts that the owner gets by using chatbot in their business, a set of features that are relevant in developing chatbot and recommendation for further implementation.

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“…Two main groups define topic models [26,49] in the literature. We have probabilistic models (PLSA (probabilistic latent semantic analysis) and LDA) and nonprobabilistic topic models such as latent semantic analysis (LSA), matrix factorization, and non-negative matrix factorization (NNMF) [50,25]. The early success of probabilistic models especially LDA has led to other extensions to enhance the flexibility of LDA.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Efficient integration of generative topic models into discriminative classifiers using robust probabilistic kernels

Ihou

Bouguila

Bouachir

2020

Pattern Anal Applic

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A direct implementation of supervised topic modeling using a Naive Bayes classifier is mainly characterized by the formulation of robust generative topic models that utilize prior distributions such as Dirichlet in LDA (latent Dirichlet allocation), where the classification ultimately follows the Bayes theorem. Though, in large scale applications, SVM (support vector machine) seems to outperform Naive Bayes. In this paper, we propose a classification framework that combines the flexibility of the generative topic models and the strong performance of the SVM. We therefore present a generative-discriminative collapsed variational Bayes technique for text documents and visual classification. Our collapsed variational Bayes topic model implements simultaneously two different and asymmetric conjugate priors within the same generative process as it specifically draws the document and corpus parameters using both GD (generalized Dirichlet) and BL (Beta-Liouville) distributions. Each of these flexible priors generalizes the Dirichlet in LDA. The proposed hybrid model results in a much improved inference that contributes to more accurate estimates, coherent (topic) generative features, a robust formulation of probabilistic kernels, and a much improved classification rate. Experiments in image and text documents classification show the merits of the proposed approach.

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Topic Modeling: A Comprehensive Review

Cited by 142 publications

References 28 publications

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

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

Chatbot Developments in The Business World

Efficient integration of generative topic models into discriminative classifiers using robust probabilistic kernels

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