A Method of Trend Analysis using Latent Dirichlet Allocation

Hwang, Myeong-Ha; Ha, Suwook; In, Minkyo; Lee, Kangchan

doi:10.14257/ijca.2018.11.5.15

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Topic modeling is thus largely independent of language and orthographic convention because it relies solely on the analyzed texts; it does not use additional sources of information such as dictionaries or external training data. It is solely based on a statistical analysis of symbol co-occurrence (at the word level), then translated into possible semantic relationships [2], [16]- [20] This paper focuses on applying LDA [2] to model the subjects from the corpus of Information Science articles based on dirichlet distribution. Each article is represented in this study as a pattern of LDA topics.…”

Section: Latent Dirichlet Allocation (Lda)mentioning

confidence: 99%

A text mining and topic modeling based bibliometric exploration of information science research

Silwattananusarn

Kulkanjanapiban

2022

IJ-AI

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This study investigates the evolution of information science research based on bibliometric analysis and semantic mining. The study discusses the value and application of metadata tagging and topic modeling. Forty-two thousand seven hundred thirty-eight articles were extracted from Clarivate Analytic's Web of Science Core Collection 2010-2020. This study was divided into two phases. Firstly, bibliometric analyzes were performed with VOSviewer. Secondly, the topic identification and evolution trends of information science research were conducted through the topic modeling approach latent dirichlet allocation (LDA) is often used to extract themes from a corpus, and the topic model was a representation of a collection of documents that is simplified using topic-modeling-toolkit (TMT). The top 10 core topics (tags) were information research design, information health-based, model data public, study information studies, analysis effect implications, knowledge support web, data research, social research study, study media information, and research impact time for the studied period. Not only does topic modeling assist in identifying popular topics or related areas within a researcher's area, but it may be used to discover emerging topics or areas of study throughout time.

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Section: Latent Dirichlet Allocation (Lda)mentioning

confidence: 99%

A text mining and topic modeling based bibliometric exploration of information science research

Silwattananusarn

Kulkanjanapiban

2022

IJ-AI

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“…The Latent Dirichlet Allocation (LDA) method was used in particular for the analysis of international standards, where the top 10 keywords were extracted using the LDA method. Also, this method made it possible to apply it in working with cloud computing to solve problems with data transfer, which is a great success in this area [9]. The Latent Dirichlet Distribution (LDA) model was used in the one-pass clustering method to extract hidden information about microblogging topics.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of a thematic and neural network model for data learning

Аkanova

Ospanova

Sharipova

et al. 2022

EEJET

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Research in the field of semantic text analysis begins with the study of the structure of natural language. The Kazakh language is unique in that it belongs to agglutinative languages and requires careful study. The object of this study is the text in the Kazakh language. Existing approaches to the study of the semantic analysis of text in the Kazakh language do not consider text analysis using the methods of thematic modeling and learning of neural networks. The purpose of this study is to determine the quality of a topic model based on the LDA (Latent Dirichlet Allocation) method with Gibbs sampling, through neural network learning. The LDA model can determine the semantic probability of the keywords of a single document and give them a rating score. To build a neural network, one of the widely used LSTM architectures was used, which has proven itself well in working with NLP (Natural Language Processing). As a result of learning, it is possible to see to what extent the text was trained and how the semantic analysis of the text in the Kazakh language went. The system, developed on the basis of the LDA model and neural network learning, combines the detected keywords into separate topics. In general, the experimental results showed that the use of deep neural networks gives the expected results of the quality of the LDA model in the processing of the Kazakh language. The developed model of the neural network contributes to the assessment of the accuracy of the semantics of the used text in the Kazakh language. The results obtained can be applied in systems for processing text data, for example, when checking the compliance of the topic and content of the proposed texts (abstracts, term papers, theses, and other works).

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“…In [36] employed LDA to understand the research trends and topics in software effort estimation. In the work proposed by [26], LDA was performed to find trends in 3962 ITU-T recommendations. The authors extracted the representative topics for each 4-year period and the trend graphs of each topic using ITU-T recommendations.…”

Section: Related Workmentioning

confidence: 99%

A Trend Analysis of Significant Topics Over Time in Machine Learning Research

et al. 2021

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A vast number of research papers on numerous topics publish every year in different conferences and journals. Thus, it is difficult for new researchers to identify research problems and topics manually, which research community is currently focusing on. Since such research problems and topics help researchers to be updated with new topics in research, it is essential to know trends in research based on topic significance over time. Therefore, in this paper, we propose a method to identify the trends in machine learning research based on significant topics over time automatically. Specifically, we apply a topic coherence model with latent Dirichlet allocation (LDA) to evaluate the optimal number of topics and significant topics for a dataset. The LDA model results in topic proportion over documents where each topic has its probability (i.e., topic weight) related to each document. Subsequently, the topic weights are processed to compute average topic weights per year, trend analysis using rolling mean, topic prevalence per year, and topic proportion per journal title. To evaluate our method, we prepare a new dataset comprising of 21,906 scientific research articles from top six journals in the area of machine learning published from 1988 to 2017. Extensive experimental results on the dataset demonstrate that our technique is efficient, and can help upcoming researchers to explore the research trends and topics in different research areas, say machine learning.

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A Method of Trend Analysis using Latent Dirichlet Allocation

Cited by 5 publications

References 3 publications

A text mining and topic modeling based bibliometric exploration of information science research

A text mining and topic modeling based bibliometric exploration of information science research

Development of a thematic and neural network model for data learning

A Trend Analysis of Significant Topics Over Time in Machine Learning Research

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