Topic Modeling Coherence: A Comparative Study between LDA and NMF Models using COVID’19 Corpus

Mifrah, Sara; Benlahmar, El Habib

doi:10.30534/ijatcse/2020/231942020

Cited by 42 publications

(22 citation statements)

References 4 publications

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“…This model requires a fixed number of topics that is not specified accurately for a corpus. Accordingly, we chose an optimal number of topics for implementing the topic modelling technique following the study by Mifrah and Benlahmar (2020). In this respect, we calculated the topic coherence score for each number of topics to identify the most efficient one.…”

Section: − Step 2: Topic Constructionmentioning

confidence: 99%

New Directions in Sensor Research: A Bibliometric Analysis for Detecting Emerging Research Fields and New Technological Trajectories

Coccia¹,

Roshani²,

Mosleh³

2021

Preprint

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The fundamental question in the field of sensor research is new directions of scientific fields, which play a vital role in the progress of science and technology. This study confronts this question here by developing a bibliometric analysis, which endeavors to explain the evolution of sensor research and new technologies that are critical to science and society. The database of Scopus concerning scientific documents and patents is used for statistical and computational analyses in these topics. Results suggest that emerging technological trajectories in sensors are wireless sensor networks, wearable sensors and biosensors. Main characteristics of these growing research fields and technologies in sensors are described for fruitful implications of research and innovation policy directed to science advances and technological change in society.

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Section: − Step 2: Topic Constructionmentioning

confidence: 99%

New Directions in Sensor Research: A Bibliometric Analysis for Detecting Emerging Research Fields and New Technological Trajectories

Coccia¹,

Roshani²,

Mosleh³

2021

Preprint

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“…It is important to clarify that c_v is a measure that helps to determine the optimal number of topics, but this measurement requires human interpretability to determine if the T topics selected are appropriate. For more extensive information on Topic Coherence measures, we encourage the reader to see [29,30].…”

Section: Determining T Optimal Number Of Topicsmentioning

confidence: 99%

Understanding Customers’ Transport Services with Topic Clustering and Sentiment Analysis

Moreno¹,

Iglesias²

2021

Applied Sciences

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The recent increase in user interaction with social media has completely changed the way customers communicate their opinions, questions, and concerns to brands. For this reason, many companies have established on the top of their agendas the necessity of analyzing the high amounts of user-generated content data in social networks. These analyses are helping brands to understand their customers’ experiences as well as for maintaining a competitive advantage in the sector. Due to this fact, this study aims to analyze and characterize the public opinions from the messages posted by Twitter users while addressing customer services. For this purpose, this study carried out a content analysis of a customer service platform. We extracted the general users’ viewpoints and sentiments of each of the discussed topics by using a wide range of techniques, such as topic modeling, document clustering, and opinion mining algorithms. For training these systems and drawing conclusions, a dataset containing tweets from the English-speaking customers addressing the @Uber_Support platform during the year 2020 has been used.

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“…Os resultados do trabalho apontam que para uma melhor coerência entre os tópicos, LDA foi a melhor abordagem. O trabalho de [Mifrah and Benlahmar 2020] também tem como tema Covid-19 e os modelos LDA e NMF, e se apoiam apenas na métrica C v . Os autores concluem que para alguns tópicos o NMF teve melhores resultados, porém, avaliando os tópicos globalmente, o LDA gera tópicos mais coerentes e concisos.…”

Section: Trabalhos Relacionadosunclassified

Modelagem Probabilística de Tópicos: Uma Comparação Empírica

Rocha¹,

Welter²,

Duarte³

2021

Anais Da XVI Escola Regional De Banco De Dados (ERBD 2021)

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Abordagens probabilísticas de tópicos são ferramentas para descobrir e explorar estruturas temáticas escondidas em coleções de textos. Dada uma coleção de documentos, a tarefa de extrair os tópicos consiste em criar um vocabulário a partir da coleção, verificar a probabilidade de cada palavra pertencer a um documento da coleção. Em seguida, baseado no número de tópicos desejado, a probabilidade de cada palavra estar associada a um determinado tópico é contabilizada. Assim, um tópico é um conjunto de palavras ordenadas pela probabilidade de estar associada ao tópico. Várias abordagens são encontradas na literatura para criação de modelos de tópicos, e.g., Hierarchical Dirichlet Process (HDP), Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF) e Dirichlet-multinomial Regression (DMR). Este trabalho procura identificar a qualidade dos tópicos construídos pelas quatro abordagens citadas. A Qualidade será medida por métricas de coerência e todas as abordagens terão a mesma coleção de documentos como entrada: notícias de websites dos jornais Breibart, Business Insider, The Atlantic, CNN e New York Times contendo 50.000 artigos. Os resultados mostram que DMR e LDA são os melhores modelos para extrair tópicos da coleção utilizada.

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Topic Modeling Coherence: A Comparative Study between LDA and NMF Models using COVID’19 Corpus

Cited by 42 publications

References 4 publications

New Directions in Sensor Research: A Bibliometric Analysis for Detecting Emerging Research Fields and New Technological Trajectories

New Directions in Sensor Research: A Bibliometric Analysis for Detecting Emerging Research Fields and New Technological Trajectories

Understanding Customers’ Transport Services with Topic Clustering and Sentiment Analysis

Modelagem Probabilística de Tópicos: Uma Comparação Empírica

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