Classification of software patches: a text mining approach

2011 ◽  
Vol 23 (2) ◽  
pp. 69-87 ◽  
Author(s):  
Uzma Raja ◽  
Marietta J. Tretter
Keyword(s):  
Text Mining

Classification of Landing and Distribution Domains Using Whois’ Text Mining

2017 ◽  
Author(s):  
Tran Phuong Thao ◽  
Akira Yamada ◽  
Kosuke Murakami ◽  
Jumpei Urakawa ◽  
Yukiko Sawaya ◽  
...  
Keyword(s):  
Text Mining

Classification of Photovoltaic Research Papers by Using Text-Mining Techniques

2013 ◽  
Vol 284-287 ◽  
pp. 3362-3369 ◽  
Author(s):  
Hyo Seong Lee ◽  
Hae Goo Song ◽  
Hee Sang Lee
Keyword(s):  
Text Mining ◽  
Solar Energy ◽  
Text Categorization ◽  
Document Clustering ◽  
Energy Research ◽  
Research Topics ◽  
Research Papers ◽  
Cell Module

The research described in this article focuses on one important aspect of monitoring scientific and technological trends and tries to examine topics of research and trends in the photovoltaic field. The data used to examine the research and trends were scientific and technological literature published during the last five years, which were exhaustively collected from the two SCI journals that specialize in photovoltaic and solar energy research. In order to analyze the 2,031 academic papers colllected, text-mining was applied. As a result, research topics were identified through document clustering and classified through text categorization into four major subjects; ‘Cell’, ‘Module/Array’, ‘System’ and ‘Relative/Advanced.’

scholarly journals BioReader: a text mining tool for performing classification of biomedical literature

2019 ◽  
Vol 19 (S13) ◽  
Author(s):  
Christian Simon ◽  
Kristian Davidsen ◽  
Christina Hansen ◽  
Emily Seymour ◽  
Mike Bogetofte Barnkob ◽  
...  
Keyword(s):  
Text Mining ◽  
Biomedical Literature ◽  
Mining Tool ◽  
Text Mining Tool

Ooredoo Rayek

2020 ◽  
Vol 11 (2) ◽  
pp. 66-81
Author(s):  
Badia Klouche ◽  
Sidi Mohamed Benslimane ◽  
Sakina Rim Bennabi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Experimental Results ◽  
Support Vector ◽  
Textual Data ◽  
New Strategy ◽  
Set Up

Sentiment analysis is one of the recent areas of emerging research in the classification of sentiment polarity and text mining, particularly with the considerable number of opinions available on social media. The Algerian Operator Telephone Ooredoo, as other operators, deploys in its new strategy to conquer new customers, by exploiting their opinions through a sentiments analysis. The purpose of this work is to set up a system called “Ooredoo Rayek”, whose objective is to collect, transliterate, translate and classify the textual data expressed by the Ooredoo operator's customers. This article developed a set of rules allowing the transliteration from Algerian Arabizi to Algerian dialect. Furthermore, the authors used Naïve Bayes (NB) and (Support Vector Machine) SVM classifiers to assign polarity tags to Facebook comments from the official pages of Ooredoo written in multilingual and multi-dialect context. Experimental results show that the system obtains good performance with 83% of accuracy.

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

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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