Topic Modeling for Customer Service Chats

Hendry, Darell; Darari, Fariz; Nurfadillah, Raditya; Khanna, Gaurav; Sun, Meng; Condylis, Paul C.; Taufik, Natanael

doi:10.1109/icacsis53237.2021.9631322

Cited by 13 publications

(11 citation statements)

References 9 publications

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“…Hence, Top2Vec can scale a large number of topics and vast quantities of data. Such strength is especially required when multiple languages emerge within a corpus (Hendry et al, 2021). The main disadvantage of Top2Vec, however, is that it is unqualified to work with a small amount of data (Abuzayed and Al-Khalifa, 2021; e.g., <1,000 documents).…”

Section: Discussionmentioning

confidence: 99%

“…In this study, a pretrained embedding models, the Universal Sentence Encoder, was used to create word and document embeddings. Since word vectors that emerge closest to the document vectors seem to best describe the topic of the document, the number of documents that can be grouped together represents the number of topics (Hendry et al, 2021).…”

Section: Model 3: Top2vecmentioning

confidence: 99%

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A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts

Egger

2022

Front. Sociol.

353

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The richness of social media data has opened a new avenue for social science research to gain insights into human behaviors and experiences. In particular, emerging data-driven approaches relying on topic models provide entirely new perspectives on interpreting social phenomena. However, the short, text-heavy, and unstructured nature of social media content often leads to methodological challenges in both data collection and analysis. In order to bridge the developing field of computational science and empirical social research, this study aims to evaluate the performance of four topic modeling techniques; namely latent Dirichlet allocation (LDA), non-negative matrix factorization (NMF), Top2Vec, and BERTopic. In view of the interplay between human relations and digital media, this research takes Twitter posts as the reference point and assesses the performance of different algorithms concerning their strengths and weaknesses in a social science context. Based on certain details during the analytical procedures and on quality issues, this research sheds light on the efficacy of using BERTopic and NMF to analyze Twitter data.

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Section: Discussionmentioning

confidence: 99%

Section: Model 3: Top2vecmentioning

confidence: 99%

A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts

Egger

2022

Front. Sociol.

353

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“…Dirichlet Allocation, LDA) 기법이 위의 선행연구를 포함해 주로 적용되어왔으나, 최근 제안된 BERTopic 기법이 다른 기 법들에 비해 성능이 월등하게 우수한 것이 알려지면서 많은 관심을 받고 있다 (Abuzayed and Al-Khalifa, 2021;Egger and Yu, 2022;Hendry et al, 2021;Grootendorst, 2022). 1).…”

Section: 토픽 모델링의 기법으로는 잠재 디리클레 할당(Latentunclassified

Issue Identification of Overseas Construction Markets from News Articles Based on BERTopic

Baik

Chung

Chi

2023

Journal of Construction Automation and Robotics

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Understanding the issues of overseas construction markets is crucial for the successful delivery of the construction projects. News articles cover various local issues and thus can be used for an analysis of the issues of the construction market. Topic modeling is the method to extract major topics from text data by grouping the text automatically, and it can be used to extract the major issues from the text data in news articles. This study applied BERTopic, a representative topic modeling method, to the extraction of issues in an overseas construction market. A total of 6,273 BBC news articles were collected for the experimental and validation purpose of the proposed BERTopic method. The results show BERTopic can effectively extract major issues from the news text and represent the issues in an easy-to-understand manner. It is expected that risks of an overseas construction project are identified in advance and mitigated by the support from BERTopic modeling.

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“…In this research, pre-trained embedding models, namely, All-MiniLM-L6-v2, Doc2Vec, and Universal Sentence Encoder, were compared based on the coherence score, and word and document embeddings were generated. Since word vectors that are closely aligned with document vectors tend to provide the most accurate representation of a document's underlying topic, the number of documents that can be grouped together indicates the number of distinct topics (Hendry et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

Ramamoorthy,

Kulothungan,

Mappillairaju

2024

Front. Artif. Intell.

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IntroductionThe utilization of social media presents a promising avenue for the prevention and management of diabetes. To effectively cater to the diabetes-related knowledge, support, and intervention needs of the community, it is imperative to attain a deeper understanding of the extent and content of discussions pertaining to this health issue. This study aims to assess and compare various topic modeling techniques to determine the most effective model for identifying the core themes in diabetes-related tweets, the sources responsible for disseminating this information, the reach of these themes, and the influential individuals within the Twitter community in India.MethodsTwitter messages from India, dated between 7 November 2022 and 28 February 2023, were collected using the Twitter API. The unsupervised machine learning topic models, namely, Latent Dirichlet Allocation (LDA), non-negative matrix factorization (NMF), BERTopic, and Top2Vec, were compared, and the best-performing model was used to identify common diabetes-related topics. Influential users were identified through social network analysis.ResultsThe NMF model outperformed the LDA model, whereas BERTopic performed better than Top2Vec. Diabetes-related conversations revolved around eight topics, namely, promotion, management, drug and personal story, consequences, risk factors and research, raising awareness and providing support, diet, and opinion and lifestyle changes. The influential nodes identified were mainly health professionals and healthcare organizations.DiscussionThe study identified important topics of discussion along with health professionals and healthcare organizations involved in sharing diabetes-related information with the public. Collaborations among influential healthcare organizations, health professionals, and the government can foster awareness and prevent noncommunicable diseases.

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Topic Modeling for Customer Service Chats

Cited by 13 publications

References 9 publications

A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts

A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts

Issue Identification of Overseas Construction Markets from News Articles Based on BERTopic

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

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