Clustering and topic modeling over tweets: A comparison over a health dataset

Lossio-Ventura, Juan Antonio; Morzán, Juandiego; Alatrista-Salas, Hugo; Hernandez‐Boussard, Tina; Bian, Jiang

doi:10.1109/bibm47256.2019.8983167

Cited by 11 publications

(2 citation statements)

References 22 publications

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“…Clustering: It is an unsupervised technique used to group the data points into a similar group. We can use clustering to group the topics [12] so that we can nd the topics within these clusters. We used the kmeans algorithm for clustering.…”

Section: Bert (Bidirectional Encoder Representations From Transformer...mentioning

confidence: 99%

An Integrated Clustering and BERT Framework for Improved Topic Modeling

George

Sumathy

2022

Preprint

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Topic modelling is a machine learning technique that is extensively used in Natural Language Processing (NLP) applications to infer topics within unstructured textual data. Latent Dirichlet Allocation (LDA) is one of the most used topic modeling techniques that can automatically detect topics from a huge collection of text documents. However, the LDA-based topic models alone do not always provide promising results. Clustering is one of the effective unsupervised machine learning algorithms that are extensively used in applications including extracting information from unstructured textual data and topic modeling. A hybrid model of Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA) in topic modeling with clustering have been studied in detail. As the clustering algorithms are computationally complex, the complexity increases with the higher number of features, the PCA, t-SNE & UMAP-based dimensionality reduction methods are also performed. Finally, a unified clustering-based framework using BERT and LDA is proposed as part of this study for mining a set of meaningful topics from the massive text corpora. The experiments are conducted to demonstrate the effectiveness of the cluster-informed topic modeling framework using BERT and LDA by simulating user input on benchmark datasets. The experimental results show that clustering would help infer more coherent topics and hence this unified clustering and BERT-LDA based approach can be effectively utilized for building topic modeling applications.

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Section: Bert (Bidirectional Encoder Representations From Transformer...mentioning

confidence: 99%

An Integrated Clustering and BERT Framework for Improved Topic Modeling

George

Sumathy

2022

Preprint

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“…The clustering is based on text representation and distance between centroid and text. While the Topic Modeling method extracts topics from a set of documents based on statistical techniques, and each topic is defined as the distribution of a set of words [5]. Topic Modeling is a technique for finding unstructured data topics [6].…”

Section: Introductionmentioning

confidence: 99%

Topic Modeling for Support Ticket using Latent Dirichlet Allocation

Wiranto

Uswatunnisa

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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In the business world, communication over customers must be built properly to make it easier for companies to find out what customers want. Support ticket is one of the business instrument for communication between the customers and the companies. Through a support ticket, customers can respond, complain or ask questions about products with a support team. Increasing the business process of the companies will be increasing the support ticket volume that should be handled by support team. It also has a value for analysis to get business intelligence decision. With that chance, an efficient data processing method is needed to find topics are being discussed by customers. One way that can be used to solve this problem is Topic Modeling. This research uses several parameters the number of topics, alpha value, beta value, iteration, and random seed. With this combination of parameters, the best results based on evaluation of human judgement and topic coherence with 5 topics, an alpha value of 50, a beta value of 0.01, 100 iterations, and 50 random seeds. The five topics interpretation consists of hosting migration, error problems in wordpress, domain email settings and domain transfer, ticketing and transaction processing. The total of 5 topics has a coherence value of 0.507897.

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Microblog Analysis with Machine Learning for Indic Languages: A Quick Survey

Roy

2021

Data Management, Analytics and Innovation

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Clustering and topic modeling over tweets: A comparison over a health dataset

Cited by 11 publications

References 22 publications

An Integrated Clustering and BERT Framework for Improved Topic Modeling

An Integrated Clustering and BERT Framework for Improved Topic Modeling

Topic Modeling for Support Ticket using Latent Dirichlet Allocation

Microblog Analysis with Machine Learning for Indic Languages: A Quick Survey

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