A scalable eigenspace-based fuzzy c-means for topic detection

Murfi, Hendri

doi:10.1108/dta-11-2020-0262

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…Topic detection methods are algorithms for detecting topics or themes in an unstructured collection of documents. This research explore the use of Bidirectional Encoder Representation from Transformer (BERT) [17] in Eigenspace-based Fuzzy C-Means (EFCM) [10][11][12]. Secondly, we use c-TFIDF [20] as topic representation instead of standard centroid method.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the clustering method is more flexible to be combined with many kinds of representation learning. One of the clustering methods for topic detection is Eigenspace-based Fuzzy C-Means (EFCM) [10][11][12]. EFCM is a clustering-based Topic Detection method consisting of two main parts, i.e., Truncated Singular Value Decomposition (TSVD) for dimension reduction and Fuzzy C-Means Clustering (FCM) for clustering.…”

Section: Introductionmentioning

confidence: 99%

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Eigenspace-based Fuzzy C-Means with Large Language Model BERT for Topic Detection

Murfi,

Agung,

Nurrohmah

et al. 2024

Preprint

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Topic detection is the process of automatically identifying topics within text data. The manual execution of this task becomes challenging for large-scale datasets due to its labor-intensive nature, and it needs machine learning for automated processing. A prevalent method in topic detection is clustering through Eigenspace-based Fuzzy C-Means (EFCM), utilizing a standard TFIDF as the text representation. However, TFIDF only pays attention to the frequency of words and does not consider the semantics of these words in the text. Bidirectional Encoder Representation from Transformer (BERT) is a pre-trained model which means that it has learnt the representations of the words and sentences as well as the underlying semantic relations that they are connected and has shown significant advantages over text representation in many Natural Language Processing (NLP) tasks. This paper extends the EFCM model using BERT instead of the standard TFIDF as text representation. Then, we use TFIDF on each cluster (c-TFIDF) to generate the most frequent words to represent the topics. Our simulations show that the BERT representation improves topic coherence scores of the EFCM model for topic detection. If we measure the topic coherence scores using TC-W2V, the BERT-based EFCM model scores better on the two datasets. Meanwhile, the model achieves better on all three datasets using the CTC measurement unit.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Eigenspace-based Fuzzy C-Means with Large Language Model BERT for Topic Detection

Murfi,

Agung,

Nurrohmah

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Autoencoder based fuzzy c-means algorithm is presented by [37]. Autoencoder is used for representation of tweets while fuzzy c-means is the clustering part of method.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Topic Detection and Tracking Techniques on Twitter: A Systematic Review

et al. 2021

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Social networks are real-time platforms formed by users involving conversations and interactions. This phenomenon of the new information era results in a very huge amount of data in different forms and modalities such as text, images, videos, and voice. The data with such characteristics are also known as big data with 5-V properties and in some cases are also referred to as social big data. To find useful information from such valuable data, many researchers tried to address different aspects of it for different modalities. In the case of text, NLP researchers conducted many research studies and scientific works to extract valuable information such as topics. Many enlightening works on different platforms of social media, like Twitter, tried to address the problem of finding important topics from different aspects and utilized it to propose solutions for diverse use cases. The importance of Twitter in this scope lies in its content and the behavior of its users. For example, it is also known as first-hand news reporting social media which has been a news reporting and informing platform even for political influencers or catastrophic news reporting. In this review article, we cover more than 50 research articles in the scope of topic detection from Twitter. We also address deep learning-based methods.

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Analysis of CEO career patterns using machine learning: taking US university graduates as an example

Hung,

Jeng,

Cheng

2024

DTA

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PurposeThis study explores the career trajectories of Chief Executive Officers (CEOs) to uncover unique characteristics that contribute to their success. By utilizing web scraping and machine learning techniques, over two thousand CEO profiles from LinkedIn are analyzed to understand patterns in their career paths. This study offers an alternative approach compared to the predominantly qualitative research methods employed in previous research.Design/methodology/approachThis study proposes a framework for analyzing CEO career patterns. Job titles and company information are encoded using the Standard Occupational Classification (SOC) scheme. The study employs the Needleman-Wunsch optimal matching algorithm and an agglomerative approach to construct distance matrices and cluster CEO career paths.FindingsThis study gathered data on the career transition processes of graduates from several renowned public and private universities in the United States via LinkedIn. Employing machine learning techniques, the analysis revealed diverse career trajectories. The findings offer career guidance for individuals from various academic backgrounds aspiring to become CEOs.Research limitations/implicationsThe building of a career sequence that takes into account the number of years requires integers. Numbers that are not integers have been rounded up to facilitate the optimal matching process but this approach prevents a perfectly accurate representation of time worked.Practical implicationsThis study makes an original contribution to the field of career pattern analysis by disclosing the distinct career path groups of CEOs using the rich LinkedIn online dataset. Note that our CEO profiles are not restricted in any industry or specific career paths followed to becoming CEOs. In light of the fact that individuals who hold CEO positions are usually perceived by society as successful, we are interested in finding the characteristics behind their success and whether either the title held or the company they remain at show patterns in making them who they are today.Originality/valueAs a matter of fact, nearly all CEOs had previous experience working for a non-Fortune organization before joining a Fortune company. Of those who have worked for Fortune firms, the number of CEOs with experience in Fortune 500 forms exceeded those with experience in Fortune 1,000 firms.

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A scalable eigenspace-based fuzzy c-means for topic detection

Cited by 5 publications

References 27 publications

Eigenspace-based Fuzzy C-Means with Large Language Model BERT for Topic Detection

Eigenspace-based Fuzzy C-Means with Large Language Model BERT for Topic Detection

Topic Detection and Tracking Techniques on Twitter: A Systematic Review

Analysis of CEO career patterns using machine learning: taking US university graduates as an example

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