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Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis: 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 2061

Special Issue Editors

Dr. Silvia García-Méndez

E-Mail Website
Guest Editor
Information Technologies Group, atlanTTic, University of Vigo, 36310 Vigo, Spain
Interests: artificial intelligence; computational linguistics; machine learning; natural language processing
Special Issues, Collections and Topics in MDPI journals
Dr. Enrique Costa-Montenegro

E-Mail Website
Guest Editor
Department of Telematics Engineering, University of Vigo, 36310 Vigo, Spain
Interests: artificial intelligence; natural language processing; P2P networks; recommender systems; personal devices and mobile services
Special Issues, Collections and Topics in MDPI journals
Dr. Francisco De Arriba-Pérez

E-Mail Website
Guest Editor
Information Technologies Group, atlanTTic, University of Vigo, 36310 Vigo, Spain
Interests: artificial intelligence; natural language processing; computing systems design; real-time systems; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The recent advancements in deep learning models and the availability of multi-modal data online have motivated the development of new natural language processing techniques. Pre-trained language models and large language models constitute representative examples. Accordingly, this Special Issue on "Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis: 2nd Edition" welcomes contributions to these advanced techniques, with particular attention being given to the management of semantic knowledge (e.g., sentiment analysis and emotion detection applications) in multidisciplinary-use cases of artificial intelligence (e.g., smart health services). It provides an opportunity to advance the generative artificial intelligence literature for academia, the industry, and the general public. Thus, the call is open for theoretical and practical applications of research trends to inspire innovation in this field. Recommended topics include, but are not limited to, the following: advanced sentiment analysis and emotion detection techniques, applications of generative artificial intelligence (e.g., pre-trained language models and large language models), machine learning models in batch and streaming operations, and the study of semantic knowledge management and representation (e.g., semantic networks), etc.

Dr. Silvia García-Méndez
Dr. Enrique Costa-Montenegro
Dr. Francisco De Arriba-Pérez
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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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
  • emotion detection
  • large language models
  • machine learning
  • natural language processing
  • pre-trained language models
  • semantics and pragmatics
  • sentiment analysis

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Published Papers (2 papers)

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Research

17 pages, 2735 KB  
Article
Relation Extraction in Spanish Medical Texts Using Deep Learning Techniques for Medical Knowledge Representation
by Gabriela A. García-Robledo, Maricela Bravo, Alma D. Cuevas-Rasgado, José A. Reyes-Ortiz and Josué Padilla-Cuevas
Appl. Sci. 2025, 15(21), 11352; https://doi.org/10.3390/app152111352 - 23 Oct 2025
Viewed by 352
Abstract
The extraction of relationships in natural language processing (NLP) is a task that consists of identifying interactions between entities within a text. This approach facilitates comprehension of context and meaning. In the medical field, this is of particular significance due to the substantial [...] Read more.
The extraction of relationships in natural language processing (NLP) is a task that consists of identifying interactions between entities within a text. This approach facilitates comprehension of context and meaning. In the medical field, this is of particular significance due to the substantial volume of information contained in scientific articles. This paper explores various training strategies for medical relationship extraction using large pre-trained language models. The findings indicate significant variations in performance between models trained with general domain data and those specialized in the medical domain. Furthermore, a methodology is proposed that utilizes language models for relation extraction with hyperparameter optimization techniques. This approach uses a triplet-based system. It provides a framework for the organization of relationships between entities and facilitates the development of medical knowledge graphs in the Spanish language. The training process was conducted using a dataset constructed and validated by medical experts. The dataset under consideration focused on relationships between entities, including anatomy, medications, and diseases. The final model demonstrated an 85.9% accuracy rate in the relationship classification task, thereby substantiating the efficacy of the proposed approach. Full article
(This article belongs to the Special Issue Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis: 2nd Edition)
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16 pages, 6601 KB  
Article
Dynamic Tuning and Multi-Task Learning-Based Model for Multimodal Sentiment Analysis
by Yi Liang, Turdi Tohti, Wenpeng Hu, Bo Kong, Dongfang Han, Tianwei Yan and Askar Hamdulla
Appl. Sci. 2025, 15(11), 6342; https://doi.org/10.3390/app15116342 - 5 Jun 2025
Viewed by 1236
Abstract
Multimodal sentiment analysis aims to uncover human affective states by integrating data from multiple sensory sources. However, previous studies have focused on optimizing model architecture, neglecting the impact of objective function settings on model performance. Given this, this study introduces a new framework, [...] Read more.
Multimodal sentiment analysis aims to uncover human affective states by integrating data from multiple sensory sources. However, previous studies have focused on optimizing model architecture, neglecting the impact of objective function settings on model performance. Given this, this study introduces a new framework, DMMSA, which utilizes the intrinsic correlation of sentiment signals and enhances the model’s understanding of complex sentiments. DMMSA incorporates coarse-grained sentiment analysis to reduce task complexity. Meanwhile, it embeds a contrastive learning mechanism within the modality, which decomposes unimodal features into similar and dissimilar ones, thus allowing for the simultaneous consideration of both unimodal and multimodal emotions. We tested DMMSA on the CH-SIMS, MOSI, and MOEI datasets. When only changing the optimization objectives, DMMSA achieved accuracy gains of 3.2%, 1.57%, and 1.95% over the baseline in five-class and seven-class classification tasks. In regression tasks, DMMSA reduced the Mean Absolute Error (MAE) by 1.46%, 1.5%, and 2.8% compared to the baseline. Full article
(This article belongs to the Special Issue Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis: 2nd Edition)
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