Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet Allocation

In the field of industrial process monitoring, scholars and practitioners are increasing interest in time-varying processes , where different phases are implemented within an unknown time frame. The measurement of process parameters could inform about the health state of the production assets, or products, but only if the measured parameters are coupled with the specific phase identification. A combination of values could be common for one phase and uncommon for another phase; thus, the same combination of values shows a high or low probability depending on the specific phase. The automatic identification of the production phase usually relies on clustering techniques. This is largely due to the difficulty of finding training fault data for supervised models. With these two considerations in mind, this contribution proposes the Latent Dirichlet Allocation as a natural language-processing technique for reviewing the topic of clustering applied in time-varying contexts, in the maintenance field. Thus, the paper presents this innovative methodology to analyze this specific research fields, presenting the step-by-step application and its results, with an overview of the theme.

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A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura

Journal of Information Science ◽

10.1177/01655515211007724 ◽

2021 ◽

pp. 016555152110077

Author(s):

Sulong Zhou ◽

Pengyu Kan ◽

Qunying Huang ◽

Janet Silbernagel

Keyword(s):

Social Media ◽

Real Time ◽

Language Processing ◽

Disaster Response ◽

Domain Knowledge ◽

Latent Dirichlet Allocation ◽

Situational Awareness ◽

High Performing ◽

Latent Topics ◽

Dirichlet Allocation

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.

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Application of Latent Dirichlet Allocation (LDA) for clustering financial tweets

E3S Web of Conferences ◽

10.1051/e3sconf/202129701071 ◽

2021 ◽

Vol 297 ◽

pp. 01071

Author(s):

Sifi Fatima-Zahrae ◽

Sabbar Wafae ◽

El Mzabi Amal

Keyword(s):

Language Processing ◽

Latent Dirichlet Allocation ◽

Sentiment Classification ◽

Research Areas ◽

Preprocessing Method ◽

Long Time ◽

Standard Text ◽

The Given ◽

Dirichlet Allocation

Sentiment classification is one of the hottest research areas among the Natural Language Processing (NLP) topics. While it aims to detect sentiment polarity and classification of the given opinion, requires a large number of aspect extractions. However, extracting aspect takes human effort and long time. To reduce this, Latent Dirichlet Allocation (LDA) method have come out recently to deal with this issue.In this paper, an efficient preprocessing method for sentiment classification is presented and will be used for analyzing user’s comments on Twitter social network. For this purpose, different text preprocessing techniques have been used on the dataset to achieve an acceptable standard text. Latent Dirichlet Allocation has been applied on the obtained data after this fast and accurate preprocessing phase. The implementation of different sentiment analysis methods and the results of these implementations have been compared and evaluated. The experimental results show that the combined uses of the preprocessing method of this paper and Latent Dirichlet Allocation have an acceptable results compared to other basic methods.

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Applying Text Mining, Clustering Analysis, and Latent Dirichlet Allocation Techniques for Topic Classification of Environmental Education Journals

Sustainability ◽

10.3390/su131910856 ◽

2021 ◽

Vol 13 (19) ◽

pp. 10856

Author(s):

I-Cheng Chang ◽

Tai-Kuei Yu ◽

Yu-Jie Chang ◽

Tai-Yi Yu

Keyword(s):

Artificial Intelligence ◽

Cluster Analysis ◽

Text Mining ◽

Environmental Education ◽

Hierarchical Clustering ◽

Language Processing ◽

Latent Dirichlet Allocation ◽

Word Analysis ◽

Dirichlet Allocation

Facing the big data wave, this study applied artificial intelligence to cite knowledge and find a feasible process to play a crucial role in supplying innovative value in environmental education. Intelligence agents of artificial intelligence and natural language processing (NLP) are two key areas leading the trend in artificial intelligence; this research adopted NLP to analyze the research topics of environmental education research journals in the Web of Science (WoS) database during 2011–2020 and interpret the categories and characteristics of abstracts for environmental education papers. The corpus data were selected from abstracts and keywords of research journal papers, which were analyzed with text mining, cluster analysis, latent Dirichlet allocation (LDA), and co-word analysis methods. The decisions regarding the classification of feature words were determined and reviewed by domain experts, and the associated TF-IDF weights were calculated for the following cluster analysis, which involved a combination of hierarchical clustering and K-means analysis. The hierarchical clustering and LDA decided the number of required categories as seven, and the K-means cluster analysis classified the overall documents into seven categories. This study utilized co-word analysis to check the suitability of the K-means classification, analyzed the terms with high TF-IDF wights for distinct K-means groups, and examined the terms for different topics with the LDA technique. A comparison of the results demonstrated that most categories that were recognized with K-means and LDA methods were the same and shared similar words; however, two categories had slight differences. The involvement of field experts assisted with the consistency and correctness of the classified topics and documents.

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Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya

PLoS ONE ◽

10.1371/journal.pone.0243208 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0243208

Author(s):

Leacky Muchene ◽

Wende Safari

Keyword(s):

Hierarchical Clustering ◽

Language Processing ◽

Latent Dirichlet Allocation ◽

Topic Modelling ◽

Two Stage ◽

Scientific Publications ◽

Statistical Tool ◽

Second Stage ◽

The University ◽

Dirichlet Allocation

Unsupervised statistical analysis of unstructured data has gained wide acceptance especially in natural language processing and text mining domains. Topic modelling with Latent Dirichlet Allocation is one such statistical tool that has been successfully applied to synthesize collections of legal, biomedical documents and journalistic topics. We applied a novel two-stage topic modelling approach and illustrated the methodology with data from a collection of published abstracts from the University of Nairobi, Kenya. In the first stage, topic modelling with Latent Dirichlet Allocation was applied to derive the per-document topic probabilities. To more succinctly present the topics, in the second stage, hierarchical clustering with Hellinger distance was applied to derive the final clusters of topics. The analysis showed that dominant research themes in the university include: HIV and malaria research, research on agricultural and veterinary services as well as cross-cutting themes in humanities and social sciences. Further, the use of hierarchical clustering in the second stage reduces the discovered latent topics to clusters of homogeneous topics.

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Topic Modeling for Keyword Extraction: using Natural Language Processing methods for keyword extraction in Portal Min@s

Revista de Estudos da Linguagem ◽

10.17851/2237-2083.23.3.695-726 ◽

2015 ◽

Vol 23 (3) ◽

pp. 695 ◽

Cited By ~ 1

Author(s):

Arnaldo Candido Junior ◽

Célia Magalhães ◽

Helena Caseli ◽

Régis Zangirolami

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Topic Modeling ◽

Latent Dirichlet Allocation ◽

Keyword Extraction ◽

Processing Methods ◽

Dirichlet Allocation

Este artigo tem o objetivo da avaliar a aplicação de dois métodos automáticos eficientes na extração de palavras-chave, usados pelas comunidades da Linguística de Corpus e do Processamento da Língua Natural para gerar palavras-chave de textos literários: o WordSmith Tools e o Latent Dirichlet Allocation (LDA). As duas ferramentas escolhidas para este trabalho têm suas especificidades e técnicas diferentes de extração, o que nos levou a uma análise orientada para a sua performance. Objetivamos entender, então, como cada método funciona e avaliar sua aplicação em textos literários. Para esse fim, usamos análise humana, com conhecimento do campo dos textos usados. O método LDA foi usado para extrair palavras-chave por meio de sua integração com o Portal Min@s: Corpora de Fala e Escrita, um sistema geral de processamento de corpora, concebido para diferentes pesquisas de Linguística de Corpus. Os resultados do experimento confirmam a eficácia do WordSmith Tools e do LDA na extração de palavras-chave de um corpus literário, além de apontar que é necessária a análise humana das listas em um estágio anterior aos experimentos para complementar a lista gerada automaticamente, cruzando os resultados do WordSmith Tools e do LDA. Também indicam que a intuição linguística do analista humano sobre as listas geradas separadamente pelos dois métodos usados neste estudo foi mais favorável ao uso da lista de palavras-chave do WordSmith Tools.

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Latent Dirichlet Allocation in predicting clinical trial terminations

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-0973-y ◽

2019 ◽

Vol 19 (1) ◽

Author(s):

Simon Geletta ◽

Lendie Follett ◽

Marcia Laugerman

Keyword(s):

Clinical Trial ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Latent Dirichlet Allocation ◽

Structured Data ◽

Unstructured Data ◽

Future Research ◽

Funding Agencies ◽

Dirichlet Allocation

Abstract Background This study used natural language processing (NLP) and machine learning (ML) techniques to identify reliable patterns from within research narrative documents to distinguish studies that complete successfully, from the ones that terminate. Recent research findings have reported that at least 10 % of all studies that are funded by major research funding agencies terminate without yielding useful results. Since it is well-known that scientific studies that receive funding from major funding agencies are carefully planned, and rigorously vetted through the peer-review process, it was somewhat daunting to us that study-terminations are this prevalent. Moreover, our review of the literature about study terminations suggested that the reasons for study terminations are not well understood. We therefore aimed to address that knowledge gap, by seeking to identify the factors that contribute to study failures. Method We used data from the clinicialTrials.gov repository, from which we extracted both structured data (study characteristics), and unstructured data (the narrative description of the studies). We applied natural language processing techniques to the unstructured data to quantify the risk of termination by identifying distinctive topics that are more frequently associated with trials that are terminated and trials that are completed. We used the Latent Dirichlet Allocation (LDA) technique to derive 25 “topics” with corresponding sets of probabilities, which we then used to predict study-termination by utilizing random forest modeling. We fit two distinct models – one using only structured data as predictors and another model with both structured data and the 25 text topics derived from the unstructured data. Results In this paper, we demonstrate the interpretive and predictive value of LDA as it relates to predicting clinical trial failure. The results also demonstrate that the combined modeling approach yields robust predictive probabilities in terms of both sensitivity and specificity, relative to a model that utilizes the structured data alone. Conclusions Our study demonstrated that the use of topic modeling using LDA significantly raises the utility of unstructured data in better predicating the completion vs. termination of studies. This study sets the direction for future research to evaluate the viability of the designs of health studies.

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Capturing and analyzing social representations. A first application of Natural Language Processing techniques to reader’s comments in COVID-19 news. Argentina, 2020

10.31235/osf.io/3pcdu ◽

2020 ◽

Author(s):

German Rosati ◽

Laia Domenech ◽

Adriana Chazarreta ◽

Tomas Maguire

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Latent Dirichlet Allocation ◽

Social Representations ◽

Web Crawler ◽

First Approximation ◽

Processing Techniques ◽

Dirichlet Allocation

We present a first approximation to the quantification of social representations about the COVID-19, using news comments. A web crawler was developed for constructing the dataset of reader’s comments. We detect relevant topics in the dataset using Latent Dirichlet Allocation, and analyze its evolution during time. Finally, we show a first prototype to the prediction of the majority topics, using FastText.

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Latent Dirichlet Allocation in Predicting Clinical Trial Terminations

10.21203/rs.2.12904/v4 ◽

2019 ◽

Author(s):

Simon Geletta ◽

Lendie Follett ◽

Marcia R Laugerman

Keyword(s):

Clinical Trial ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Latent Dirichlet Allocation ◽

Structured Data ◽

Unstructured Data ◽

Future Research ◽

Funding Agencies ◽

Dirichlet Allocation

Abstract This study used natural language processing (NLP) and machine learning (ML) techniques to identify reliable patterns from within research narrative documents to distinguish studies that complete successfully, from the ones that terminate. Recent research findings have reported that at least ten percent of all studies that are funded by major research funding agencies terminate without yielding useful results. Since it is well-known that scientific studies that receive funding from major funding agencies are carefully planned, and rigorously vetted through the peer-review process, it was somewhat daunting to us that study-terminations are this prevalent. Moreover, our review of the literature about study terminations suggested that the reasons for study terminations are not well understood. We therefore aimed to address that knowledge gap, by seeking to identify the factors that contribute to study failures.Method: We used data from the clinicialTrials.gov repository, from which we extracted both structured data (study characteristics), and unstructured data (the narrative description of the studies). We applied natural language processing techniques to the unstructured data to quantify the risk of termination by identifying distinctive topics that are more frequently associated with trials that are terminated and trials that are completed. We used the Latent Dirichlet Allocation (LDA) technique to derive 25 “topics” with corresponding sets of probabilities, which we then used to predict study-termination by utilizing random forest modeling. We fit two distinct models – one using only structured data as predictors and another model with both structured data and the 25 text topics derived from the unstructured data.Results: In this paper, we demonstrate the interpretive and predictive value of LDA as it relates to predicting clinical trial failure. The results also demonstrate that the combined modeling approach yields robust predictive probabilities in terms of both sensitivity and specificity, relative to a model that utilizes the structured data alone.Conclusions: Our study demonstrated that the use of topic modeling using LDA significantly raises the utility of unstructured data in better predicating the completion vs. termination of studies. This study sets the direction for future research to evaluate the viability of the designs of health studies.

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Klasifikasi Laporan Keluhan Pelayanan Publik Berdasarkan Instansi Menggunakan Metode LDA-SVM

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021863768 ◽

2021 ◽

Vol 8 (6) ◽

pp. 1265

Author(s):

Muhammad Alkaff ◽

Andreyan Rizky Baskara ◽

Irham Maulani

Keyword(s):

Support Vector Machine ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Latent Dirichlet Allocation ◽

Confusion Matrix ◽

Support Vector ◽

Topic Distribution ◽

The Government ◽

Dirichlet Allocation

Sebuah sistem layanan untuk menyampaikan aspirasi dan keluhan masyarakat terhadap layanan pemerintah Indonesia, bernama Lapor! Pemerintah sudah lama memanfaatkan sistem tersebut untuk menjawab permasalahan masyarakat Indonesia terkait permasalahan birokrasi. Namun, peningkatan volume laporan dan pemilahan laporan yang dilakukan oleh operator dengan membaca setiap keluhan yang masuk melalui sistem menyebabkan sering terjadi kesalahan dimana operator meneruskan laporan tersebut ke instansi yang salah. Oleh karena itu, diperlukan suatu solusi yang dapat menentukan konteks laporan secara otomatis dengan menggunakan teknik Natural Language Processing. Penelitian ini bertujuan untuk membangun klasifikasi laporan secara otomatis berdasarkan topik laporan yang ditujukan kepada instansi yang berwenang dengan menggabungkan metode Latent Dirichlet Allocation (LDA) dan Support Vector Machine (SVM). Proses pemodelan topik untuk setiap laporan dilakukan dengan menggunakan metode LDA. Metode ini mengekstrak laporan untuk menemukan pola tertentu dalam dokumen yang akan menghasilkan keluaran dalam nilai distribusi topik. Selanjutnya, proses klasifikasi untuk menentukan laporan agensi tujuan dilakukan dengan menggunakan SVM berdasarkan nilai topik yang diekstraksi dengan metode LDA. Performa model LDA-SVM diukur dengan menggunakan confusion matrix dengan menghitung nilai akurasi, presisi, recall, dan F1 Score. Hasil pengujian menggunakan teknik split train-test dengan skor 70:30 menunjukkan bahwa model menghasilkan kinerja yang baik dengan akurasi 79,85%, presisi 79,98%, recall 72,37%, dan Skor F1 74,67%. AbstractA service system to convey aspirations and complaints from the public against Indonesia's government services, named Lapor! The Government has used the Government for a long time to answer the problems of the Indonesian people related to bureaucratic problems. However, the increasing volume of reports and the sorting of reports carried out by operators by reading every complaint that comes through the system cause frequent errors where operators forward the reports to the wrong agencies. Therefore, we need a solution that can automatically determine the report's context using Natural Language Processing techniques. This study aims to build automatic report classifications based on report topics addressed to authorized agencies by combining Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM). The topic-modeling process for each report was carried out using the LDA method. This method extracts reports to find specific patterns in documents that will produce output in topic distribution values. Furthermore, the classification process to determine the report's destination agency carried out using the SVM based on the value of the topics extracted by the LDA method. The LDA-SVM model's performance is measured using a confusion matrix by calculating the value of accuracy, precision, recall, and F1 Score. The test results using the train-test split technique with a 70:30 show that the model produces good performance with 79.85% accuracy, 79.98% precision, 72.37% recall, and 74.67% F1 Score

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Latent Dirichlet Allocation in Predicting Clinical Trial Terminations

10.21203/rs.2.12904/v3 ◽

2019 ◽

Author(s):

Simon Geletta ◽

Lendie Follett ◽

Marcia R Laugerman

Keyword(s):

Clinical Trial ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Latent Dirichlet Allocation ◽

Structured Data ◽

Unstructured Data ◽

Future Research ◽

Funding Agencies ◽

Dirichlet Allocation

Abstract Background: This study used natural language processing (NLP) and machine learning (ML) techniques to identify reliable patterns from within research narrative documents to distinguish studies that complete successfully, from the ones that terminate. Recent research findings have reported that at least ten percent of all studies that are funded by major research funding agencies terminate without yielding useful results. Since it is well-known that scientific studies that receive funding from major funding agencies are carefully planned, and rigorously vetted through the peer-review process, it was somewhat daunting to us that study-terminations are this prevalent. Moreover, our review of the literature about study terminations suggested that the reasons for study terminations are not well understood. We therefore aimed to address that knowledge gap, by seeking to identify the factors that contribute to study failures.Method: We used data from the clinicialTrials.gov repository, from which we extracted both structured data (study characteristics), and unstructured data (the narrative description of the studies). We applied natural language processing techniques to the unstructured data to quantify the risk of termination by identifying distinctive topics that are more frequently associated with trials that are terminated and trials that are completed. We used the Latent Dirichlet Allocation (LDA) technique to derive 25 “topics” with corresponding sets of probabilities, which we then used to predict study-termination by utilizing random forest modeling. We fit two distinct models – one using only structured data as predictors and another model with both structured data and the 25 text topics derived from the unstructured data.Results: In this paper, we demonstrate the interpretive and predictive value of LDA as it relates to predicting clinical trial failure. The results also demonstrate that the combined modeling approach yields robust predictive probabilities in terms of both sensitivity and specificity, relative to a model that utilizes the structured data alone.Conclusions: Our study demonstrated that the use of topic modeling using LDA significantly raises the utility of unstructured data in better predicating the completion vs. termination of studies. This study sets the direction for future research to evaluate the viability of the designs of health studies.

Download Full-text