Topic Modeling on Indonesian Online Shop Chat

Hidayatullah, Ahmad Fathan; Kurniawan, Wisnu; Ratnasari, Chanifah Indah

doi:10.1145/3342827.3342831

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…Secondly, preprocessing is carried out to remove unnecessary parts from the text. After preprocessing, we applied phrase identification to identify phrases from texts [17]. The next step is applying term weighting for each token using TF-IDF (Term Frequency-Inverse Document Frequency).…”

Section: Methodsmentioning

confidence: 99%

“…The phrase identification is carried out by counting the occurrences of two or three words that appear together. If the consecutive words appear together at least five times, they would be considered as a phrase by adding underscore between the consecutive words [17].…”

Section: Phrase Identificationmentioning

confidence: 99%

“…Moreover, the topic modeling task will be conducted based on titles and the year of publication. To obtain better topic modeling results, we also propose TF-IDF (Term Frequency-Inverse Document Frequency) and phrase identification to be applied before the topic modeling task [16], [17]. The objective of this study is to facilitate academicians in obtaining the trends and illustration of research topics in the field of computer science and information technology in Indonesia.…”

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confidence: 99%

“…During the topic modeling task using LDA, we calculate a coherence score to obtain the most appropriate number of topics [17]. Topic coherence calculates a particular topic by computing the degree of semantic closeness between high-scoring terms in the topic [21].…”

mentioning

confidence: 99%

“…Topic coherence calculates a particular topic by computing the degree of semantic closeness between high-scoring terms in the topic [21]. We determined the best number of topics based on the highest coherence score [17]. In this study, we employed the measure as a method for measuring coherence.…”

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confidence: 99%

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Discovering Computer Science Research Topic Trends using Latent Dirichlet Allocation

Nastiti

Hidayatullah

Pratama

2021

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Before conducting a research project, researchers must find the trends and state of the art in their research field. However, that is not necessarily an easy job for researchers, partly due to the lack of specific tools to filter the required information by time range. This study aims to provide a solution to that problem by performing a topic modeling approach to the scraped data from Google Scholar between 2010 and 2019. We utilized Latent Dirichlet Allocation (LDA) combined with Term Frequency-Indexed Document Frequency (TF-IDF) to build topic models and employed the coherence score method to determine how many different topics there are for each year’s data. We also provided a visualization of the topic interpretation and word distribution for each topic as well as its relevance using word cloud and PyLDAvis. In the future, we expect to add more features to show the relevance and interconnections between each topic to make it even easier for researchers to use this tool in their research projects.

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Section: Methodsmentioning

confidence: 99%

Section: Phrase Identificationmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

mentioning

confidence: 99%

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Discovering Computer Science Research Topic Trends using Latent Dirichlet Allocation

Nastiti

Hidayatullah

Pratama

2021

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Hot issue in health use topic modelling with LDA

Danuri,

Jaroji

2023

AIP Conference Proceedings

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Knowledge discovery of zakat administration worldwide from YouTube and Zoom via text mining

Hudaefi

Hassan²,

Abduh³

et al. 2022

JIABR

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Purpose Zakat (Islamic almsgiving) plays a considerable role in dealing with the socioeconomic issues in times of COVID-19 pandemic, and such roles have been widely discussed in virtual events. This paper aims to discover knowledge of the current global zakat administration from virtual events of zakat (e.g. webinars) on YouTube and Zoom via text mining approach. Design/methodology/approach The authors purposefully sampled 12 experts from four different virtual zakat events on YouTube and Zoom. The automated text transcription software is used to pull the information from the sampled videos into text documents. A qualitative analysis is operated using text mining approach via machine learning tool (i.e. Orange Data Mining). Four research questions are developed under the Word Cloud visualisation, hierarchal clustering, topic modelling and graph and network theory. Findings The machine learning identifies the most important words, the relationship between the experts and their top words and discovers hidden themes from the sample. This finding is practically substantial for zakat stakeholders to understand the current issues of global zakat administration and to learn the applicable lessons from the current issues of zakat management worldwide. Research limitations/implications This study does not establish a positivist generalisation from the findings because of the nature and objective of the study. Practical implications A policy implication is drawn pertaining to the legislation of zakat as an Islamic financial policy instrument for combating poverty in Muslim society. Social implications This work supports the notion of “socioeconomic zakat”, implying that zakat as a religious obligation is important in shaping the social and economic processes of a Muslim community. Originality/values This work marks the novelty in making sense of the unstructured data from virtual events on YouTube and Zoom in the Islamic social finance research.

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Topic Modeling on Indonesian Online Shop Chat

Cited by 7 publications

References 8 publications

Discovering Computer Science Research Topic Trends using Latent Dirichlet Allocation

Discovering Computer Science Research Topic Trends using Latent Dirichlet Allocation

Hot issue in health use topic modelling with LDA

Knowledge discovery of zakat administration worldwide from YouTube and Zoom via text mining

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