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Information
Special Issues
Machine Learning and Data Mining for User Classification
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Machine Learning and Data Mining for User Classification

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2053

Special Issue Editors

Dr. Ioannis Tsimperidis

E-Mail Website
Guest Editor
Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Interests: keystroke dynamics; user classification; machine learning; data mining
Dr. Eleni Vrochidou

E-Mail Website
Guest Editor
Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Interests: signal processing; intelligent systems; pattern recognition; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. George A. Papakostas

E-Mail Website
Guest Editor
MLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Interests: pattern recognition; computer/machine vision; computational intelligence; machine learning; feature extraction; evolutionary optimization; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The modern Internet is characterized by its many users, the multitude of web services offered, and the increased complexity of accessing digital resources. In this context, an increased number and more sophisticated threats have occurred, requiring new methods for protecting and facilitating users. Also, the large amount of stored raw data, which is growing at a dizzying pace daily, hides information that is not immediately available and requires time and effort.

This Special Issue suggests new approaches for creating user profiles to protect unsuspecting users, enhancing user authentication, and extracting information from textual data. Moreover, this Special Issue is interested in document classification and, generally, for methods protecting Internet users, making better use of the services offered, and extracting the available information using data derived mainly from text and typing.

Dr. Ioannis Tsimperidis
Dr. Eleni Vrochidou
Prof. Dr. George A. Papakostas
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

  • data mining
  • information retrieval
  • text analysis
  • data clustering
  • user authentication
  • user profiling
  • user classification by inherent and acquired characteristics
  • natural language processing
  • author classification
  • keystroke dynamics
  • typing pattern recognition
  • content classification
  • digital text forensics
  • typing behavior

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Published Papers (2 papers)

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33 pages, 6468 KiB  
Article
Exploring Sentiment Analysis for the Indonesian Presidential Election Through Online Reviews Using Multi-Label Classification with a Deep Learning Algorithm
by Ahmad Nahid Ma’aly, Dita Pramesti, Ariadani Dwi Fathurahman and Hanif Fakhrurroja
Information 2024, 15(11), 705; https://doi.org/10.3390/info15110705 - 5 Nov 2024
Viewed by 626
Abstract
Presidential elections are an important political event that often trigger intense debate. With more than 139 million users, YouTube serves as a significant platform for understanding public opinion through sentiment analysis. This study aimed to implement deep learning techniques for a multi-label sentiment [...] Read more.
Presidential elections are an important political event that often trigger intense debate. With more than 139 million users, YouTube serves as a significant platform for understanding public opinion through sentiment analysis. This study aimed to implement deep learning techniques for a multi-label sentiment analysis of comments on YouTube videos related to the 2024 Indonesian presidential election. Offering a fresh perspective compared to previous research that primarily employed traditional classification methods, this study classifies comments into eight emotional labels: anger, anticipation, disgust, joy, fear, sadness, surprise, and trust. By focusing on the emotional spectrum, this study provides a more nuanced understanding of public sentiment towards presidential candidates. The CRISP-DM method is applied, encompassing stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment, ensuring a systematic and comprehensive approach. This study employs a dataset comprising 32,000 comments, obtained via YouTube Data API, from the KPU and Najwa Shihab channels. The analysis is specifically centered on comments related to presidential candidate debates. Three deep learning models—Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and a hybrid model combining CNN and Bi-LSTM—are assessed using confusion matrix, Area Under the Curve (AUC), and Hamming loss metrics. The evaluation results demonstrate that the Bi-LSTM model achieved the highest accuracy with an AUC value of 0.91 and a Hamming loss of 0.08, indicating an excellent ability to classify sentiment with high precision and a low error rate. This innovative approach to multi-label sentiment analysis in the context of the 2024 Indonesian presidential election expands the insights into public sentiment towards candidates, offering valuable implications for political campaign strategies. Additionally, this research contributes to the fields of natural language processing and data mining by addressing the challenges associated with multi-label sentiment analysis. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining for User Classification)
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12 pages, 583 KiB  
Article
IKDD: A Keystroke Dynamics Dataset for User Classification
by Ioannis Tsimperidis, Olga-Dimitra Asvesta, Eleni Vrochidou and George A. Papakostas
Information 2024, 15(9), 511; https://doi.org/10.3390/info15090511 - 23 Aug 2024
Viewed by 860
Abstract
Keystroke dynamics is the field of computer science that exploits data derived from the way users type. It has been used in authentication systems, in the identification of user characteristics for forensic or commercial purposes, and to identify the physical and mental state [...] Read more.
Keystroke dynamics is the field of computer science that exploits data derived from the way users type. It has been used in authentication systems, in the identification of user characteristics for forensic or commercial purposes, and to identify the physical and mental state of users for purposes that serve human–computer interaction. Studies of keystroke dynamics have used datasets created from volunteers recording fixed-text typing or free-text typing. Unfortunately, there are not enough keystroke dynamics datasets available on the Internet, especially from the free-text category, because they contain sensitive and personal information from the volunteers. In this work, a free-text dataset is presented, which consists of 533 logfiles, each of which contains data from 3500 keystrokes, coming from 164 volunteers. Specifically, the software developed to record user typing is described, the demographics of the volunteers who participated are given, the structure of the dataset is analyzed, and the experiments performed on the dataset justify its utility. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining for User Classification)
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