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Information
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Text Mining: Classification, Clustering and Extraction Techniques
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Text Mining: Classification, Clustering and Extraction Techniques

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 19242

Special Issue Editors

Prof. Dr. Byung-Won On

E-Mail Website
Guest Editor
Department of Software Convergence Engineering, Kunsan National University, Gunsan-si, Jeollabuk-do, Korea
Interests: text mining, natural language processing, information retrieval, transfer learning, reinforcement learning
Dr. Jose G Moreno

E-Mail Website
Guest Editor
University of Toulouse - IRIT, 31000 Toulouse, France
Interests: information retrieval; natural language processing; information extraction

Special Issue Information

Dear Colleagues,

Pre-trained language models such as BERT and GPT-3 have excellent performance in handling various kinds of natural language processing tasks such as question answering, machine translation, summarization, sentiment analysis, and so on. Meanwhile, in order to reduce the gap between the loss function used in deep learning and the objective function for actual machine translation, along with the great success of machine translation, the policy gradient method of reinforcement learning can be used to receive compensation from the objective function in the actual natural language generation. As soon as there was, the ability to generate sentences that seemed to be used by real people was further maximized. In these cases, it is expected that the recent success of natural language processing techniques through deep learning and reinforcement learning can be transfered to main text mining problems including text clustering, text classification, and text extraction.

Therefore, the purpose of this Special Issue is to present the latest text mining methods based on deep learning and reinforcement learning to address main text mining problems such as text classification, text clustering, and text extraction. Investigators in the field are invited to contributed with their original, unpublished works. Both research and review papers are welcome.

Prof. Dr. Byung-Won On
Dr. Jose G Moreno
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Text Mining
  • Natural Language Processing
  • Deep Learning
  • Reinforcement Learning

Published Papers (4 papers)

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Research

30 pages, 1907 KiB  
Article
A Generic Graph-Based Method for Flexible Aspect-Opinion Analysis of Complex Product Customer Feedback
by Michael Y. Kpiebaareh, Wei-Ping Wu, Brighter Agyemang, Charles R. Haruna and Tandoh Lawrence
Information 2022, 13(3), 118; https://doi.org/10.3390/info13030118 - 28 Feb 2022
Cited by 4 | Viewed by 2840
Abstract
Product design experts depend on online customer reviews as a source of insight to improve product design. Previous works used aspect-based sentiment analysis to extract insight from product reviews. However, their approaches for requirements elicitation are less flexible than traditional tools such as [...] Read more.
Product design experts depend on online customer reviews as a source of insight to improve product design. Previous works used aspect-based sentiment analysis to extract insight from product reviews. However, their approaches for requirements elicitation are less flexible than traditional tools such as interviews and surveys. They require costly data labeling or pre-labeled datasets, lack domain knowledge integration, and focus more on sentiment classification than flexible aspect-opinion analysis. Related works lack effective mechanisms for probing the customer feedback of complex configurable products. This study proposes a generic graph-based opinion mining and analysis method for product design improvement. First, a customer feedback data preprocessing and annotation pipeline that can incorporate designer-specified domain knowledge is proposed. Second, an intuitive opinion-aware labeled property graph data model is designed to ingest preprocessed feedback data and perform ad hoc opinion analysis. Applying the generic model to a real-world dataset demonstrates superior functionality and flexibility compared to related works. A wider range of analyses is supported in a single model without repeating data preprocessing and modeling. Specifically, the proposed method supports regular and comparative aspect-opinion analysis, aspect satisfaction/influence ranking, opinion trend extraction, and targeted aspect-opinion summarization. Full article
(This article belongs to the Special Issue Text Mining: Classification, Clustering and Extraction Techniques)
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16 pages, 781 KiB  
Article
Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites
by Zhiyong Xiong, Zhaoxiong Yan, Huanan Yao and Shangsong Liang
Information 2022, 13(3), 110; https://doi.org/10.3390/info13030110 - 25 Feb 2022
Cited by 1 | Viewed by 2441
Abstract
In order to facilitate designers to explore the market demand trend of laptops and to establish a better “network users-market feedback mechanism”, we propose a design and research method of a short text mining tool based on the K-means clustering algorithm and Kano [...] Read more.
In order to facilitate designers to explore the market demand trend of laptops and to establish a better “network users-market feedback mechanism”, we propose a design and research method of a short text mining tool based on the K-means clustering algorithm and Kano mode. An improved short text clustering algorithm is used to extract the design elements of laptops. Based on the traditional questionnaire, we extract the user’s attention factors, score the emotional tendency, and analyze the user’s needs based on the Kano model. Then, we select 10 laptops, process them by the improved algorithm, cluster the evaluation words and quantify the emotional orientation matching. Based on the obtained data, we design a visual interaction logic and usability test. These prove that the proposed method is feasible and effective. Full article
(This article belongs to the Special Issue Text Mining: Classification, Clustering and Extraction Techniques)
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13 pages, 1473 KiB  
Article
Analysis of Unsatisfying User Experiences and Unmet Psychological Needs for Virtual Reality Exergames Using Deep Learning Approach
by Xiaoyan Zhang, Qiang Yan, Simin Zhou, Linye Ma and Siran Wang
Information 2021, 12(11), 486; https://doi.org/10.3390/info12110486 - 22 Nov 2021
Cited by 3 | Viewed by 1869
Abstract
The number of consumers playing virtual reality games is booming. To speed up product iteration, the user experience team needs to collect and analyze unsatisfying experiences in time. In this paper, we aim to detect the unsatisfying experiences hidden in online reviews of [...] Read more.
The number of consumers playing virtual reality games is booming. To speed up product iteration, the user experience team needs to collect and analyze unsatisfying experiences in time. In this paper, we aim to detect the unsatisfying experiences hidden in online reviews of virtual reality exergames using a deep learning method and find out the unmet psychological needs of users based on self-determination theory. Convolutional neural networks for sentence classification (textCNN) are used in this study to classify online reviews with unsatisfying experiences. For comparison, we set eXtreme gradient boosting (XGBoost) with lexical features as the baseline of machine learning. Term frequency-inverse document frequency (TF-IDF) is used to extract keywords from every set of classified reviews. The micro-F1 score of textCNN classifier is 90.00, which is better than 82.69 of XGBoost. The top 10 keywords of every set of reviews reflect relevant topics of unmet psychological needs. This paper explores the potential problems causing unsatisfying experiences and unmet psychological needs in virtual reality exergames through text mining and makes a supplement for experimental studies about virtual reality exergames. Full article
(This article belongs to the Special Issue Text Mining: Classification, Clustering and Extraction Techniques)
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15 pages, 4127 KiB  
Article
Text Mining and Sentiment Analysis of Newspaper Headlines
by Arafat Hossain, Md. Karimuzzaman, Md. Moyazzem Hossain and Azizur Rahman
Information 2021, 12(10), 414; https://doi.org/10.3390/info12100414 - 09 Oct 2021
Cited by 11 | Viewed by 10513
Abstract
Text analytics are well-known in the modern era for extracting information and patterns from text. However, no study has attempted to illustrate the pattern and priorities of newspaper headlines in Bangladesh using a combination of text analytics techniques. The purpose of this paper [...] Read more.
Text analytics are well-known in the modern era for extracting information and patterns from text. However, no study has attempted to illustrate the pattern and priorities of newspaper headlines in Bangladesh using a combination of text analytics techniques. The purpose of this paper is to examine the pattern of words that appeared on the front page of a well-known daily English newspaper in Bangladesh, The Daily Star, in 2018 and 2019. The elucidation of that era’s possible social and political context was also attempted using word patterns. The study employs three widely used and contemporary text mining techniques: word clouds, sentiment analysis, and cluster analysis. The word cloud reveals that election, kill, cricket, and Rohingya-related terms appeared more than 60 times in 2018, whereas BNP, poll, kill, AL, and Khaleda appeared more than 80 times in 2019. These indicated the country’s passion for cricket, political turmoil, and Rohingya-related issues. Furthermore, sentiment analysis reveals that words of fear and negative emotions appeared more than 600 times, whereas anger, anticipation, sadness, trust, and positive-type emotions came up more than 400 times in both years. Finally, the clustering method demonstrates that election, politics, deaths, digital security act, Rohingya, and cricket-related words exhibit similarity and belong to a similar group in 2019, whereas rape, deaths, road, and fire-related words clustered in 2018 alongside a similar-appearing group. In general, this analysis demonstrates how vividly the text mining approach depicts Bangladesh’s social, political, and law-and-order situation, particularly during election season and the country’s cricket craze, and also validates the significance of the text mining approach to understanding the overall view of a country during a particular time in an efficient manner. Full article
(This article belongs to the Special Issue Text Mining: Classification, Clustering and Extraction Techniques)
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