Harnessing Artificial Intelligence for Social and Semantic Understanding

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 550

Special Issue Editors


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Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, Egaleo, Greece
Interests: IoT; data and social mining; environmental monitoring; semantics; knowledge representation; spatio-temporal

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: social net artificial intellworking services; sentiment analysis; learning analytics; user modeling andigence
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: knowledge management; context representation and analysis; knowledge-assisted multimedia analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today's data-rich landscape, the demand for efficient artificial intelligence (AI) models, tools, and applications for data mining and machine learning is more pronounced than ever. The proliferation of social and semantic networks has generated a wealth of valuable information, presenting both opportunities and challenges. Researchers grapple with novel approaches to address emerging research questions, particularly in the realms of social information processing and semantic applications. The proposed Special Issue and the SMAP workshop series revolve around two central themes: first, the extraction, processing, and analysis of data, information, and knowledge using AI techniques; and second, the application of these results to construct effective models and tools. Ultimately, this interdisciplinary endeavor aims to foster diverse behaviors associated with AI activities, bridging the gap between social and semantic impact. Computer scientists are encouraged to contribute innovative AI solutions to tackle the inherent challenges posed by dynamic and semantically heterogeneous computational data. The call for relevant research manuscripts is open to all interested parties.

The content of the proposed Special Issue and the goals of the SMAP workshop series are organized around two main themes. The first theme focuses on efficiently extracting, processing, manipulating and analysing data, information and knowledge with the utilization of artificial intelligence in the process, while the second theme focuses on using the above results to effectively build models, tools and applications. The ultimate goal, of course, is to promote a variety of machine and/or human behaviours associated with related artificial intelligence activities.

In addition to the Open Call, selected papers that will be presented during SMAP 2024 will be invited to be submitted as extended versions to this Special Issue. In this case, the workshop paper should be cited and noted on the first page of the submitted paper; authors are asked to disclose that it is a workshop paper in their Cover Letter and include a statement on what has been changed compared to the original workshop paper. Each submission to this journal Special Issue should contain at least 50% new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases.

All submitted papers will undergo standard peer-review procedures. Accepted papers will be published in open access format in Computers and available on the Special Issue’s website.

Dr. Yorghos Voutos
Dr. Akrivi Krouska
Dr. Christos Troussas
Dr. Phivos Mylonas
Prof. Dr. Cleo Sgouropoulou
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. Computers 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 1800 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

  • artificial intelligence (AI)
  • social network
  • semantic network

Published Papers (1 paper)

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Research

14 pages, 1352 KiB  
Article
MTL-AraBERT: An Enhanced Multi-Task Learning Model for Arabic Aspect-Based Sentiment Analysis
by Arwa Fadel, Mostafa Saleh, Reda Salama and Osama Abulnaja
Computers 2024, 13(4), 98; https://doi.org/10.3390/computers13040098 - 15 Apr 2024
Viewed by 388
Abstract
Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis; it works on an aspect level. It mainly focuses on extracting aspect terms from text or reviews, categorizing the aspect terms, and classifying the sentiment polarities toward each aspect term and aspect [...] Read more.
Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis; it works on an aspect level. It mainly focuses on extracting aspect terms from text or reviews, categorizing the aspect terms, and classifying the sentiment polarities toward each aspect term and aspect category. Aspect term extraction (ATE) and aspect category detection (ACD) are interdependent and closely associated tasks. However, the majority of the current literature on Arabic aspect-based sentiment analysis (ABSA) deals with these tasks individually, assumes that aspect terms are already identified, or employs a pipeline model. Pipeline solutions employ single models for each task, where the output of the ATE model is utilized as the input for the ACD model. This sequential process can lead to the propagation of errors across different stages, as the performance of the ACD model is influenced by any errors produced by the ATE model. Therefore, the primary objective of this study was to investigate a multi-task learning approach based on transfer learning and transformers. We propose a multi-task learning model (MTL) that utilizes the pre-trained language model (AraBERT), namely, the MTL-AraBERT model, for extracting Arabic aspect terms and aspect categories simultaneously. Specifically, we focused on training a single model that simultaneously and jointly addressed both subtasks. Moreover, this paper also proposes a model integrating AraBERT, single pair classification, and BiLSTM/BiGRU that can be applied to aspect term polarity classification (APC) and aspect category polarity classification (ACPC). All proposed models were evaluated using the SemEval-2016 annotated dataset for the Arabic hotel dataset. The experiment results of the MTL model demonstrate that the proposed models achieved comparable or better performance than state-of-the-art works (F1-scores of 80.32% for the ATE and 68.21% for the ACD). The proposed SPC-BERT model demonstrated high accuracy, reaching 89.02% and 89.36 for APC and ACPC, respectively. These improvements hold significant potential for future research in Arabic ABSA. Full article
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