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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: 30 December 2026 | Viewed by 10208

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-anonymized 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 (6 papers)

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29 pages, 4041 KB  
Article
Using LLMs for Pre-Annotation of Emotional Manipulation Techniques in a Low-Resource Language Corpus: Are We There Yet?
by Rita Butkienė, Algirdas Šukys, Edgaras Dambrauskas, Voldemaras Žitkus, Linas Ablonskis, Evaldas Vaičiukynas, Paulius Danėnas and Rimantas Butleris
Appl. Sci. 2026, 16(12), 6251; https://doi.org/10.3390/app16126251 (registering DOI) - 22 Jun 2026
Viewed by 85
Abstract
This paper examines whether incremental prompt engineering can enable reliable large language model (LLM)-based pre-annotation of corpus texts in a low-resource language setting. Using Lithuanian as a case study, we systematically evaluate multiple LLM prompt designs and assess their suitability for generating emotional [...] Read more.
This paper examines whether incremental prompt engineering can enable reliable large language model (LLM)-based pre-annotation of corpus texts in a low-resource language setting. Using Lithuanian as a case study, we systematically evaluate multiple LLM prompt designs and assess their suitability for generating emotional manipulation annotations for corpus development. We find that performance varies with task complexity, and systematic prompt refinement measurably reduces output instability. Cross-model evaluation of the best-performing prompting strategy shows consistent and similar trends over several modern LLMs. Our results demonstrate that while structured prompts substantially improve output consistency and LLM-assisted annotation can roughly approximate human-produced labels for well-defined categories, the quality of results produced by contemporary LLMs is unsatisfactory for automatic pre-annotation of emotional manipulation techniques in a low-resource language. 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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25 pages, 1893 KB  
Article
Contribution to Sarcasm Detection in Arabic Using Natural Language Processing Techniques
by Mennat Allah Hassan, Silvia García-Méndez and Francisco de Arriba-Pérez
Appl. Sci. 2026, 16(6), 2724; https://doi.org/10.3390/app16062724 - 12 Mar 2026
Viewed by 714
Abstract
Sarcasm detection remains a challenging task in Natural Language Processing (nlp), especially for low-resource and non-standardized languages. Hence, this study addresses Franco-Arabic, a widely used form of online communication where Arabic words are written with Latin characters and numerals. Its informal [...] Read more.
Sarcasm detection remains a challenging task in Natural Language Processing (nlp), especially for low-resource and non-standardized languages. Hence, this study addresses Franco-Arabic, a widely used form of online communication where Arabic words are written with Latin characters and numerals. Its informal nature and orthographic variation complicate sarcasm identification and limit the applicability of existing nlp models. We propose an approach that integrates transformer-based representations with auxiliary linguistic features and rule-based cues to capture both contextual meaning and sentiment-driven inconsistencies. This research opens the door to practical applications. In particular, future work will investigate integrating sarcasm detection into the marketing sector, where accurate recognition of sarcastic reviews can enhance sentiment analysis, customer segmentation, and personalized communication strategies. 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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27 pages, 2316 KB  
Article
Linking Customer Sentiment to Patent-Based Solutions Through Semantic Analysis
by Sakire Nesli Demircioglu, Serkan Altuntas and Turkay Dereli
Appl. Sci. 2026, 16(5), 2570; https://doi.org/10.3390/app16052570 - 7 Mar 2026
Viewed by 897
Abstract
Analyzing customer feedback is critical for identifying unmet needs in product development and innovation processes. However, current studies often focus only on identifying customer-expressed problems, neglecting to systematically match these problems with technological solutions and transform them into potential product features. This study [...] Read more.
Analyzing customer feedback is critical for identifying unmet needs in product development and innovation processes. However, current studies often focus only on identifying customer-expressed problems, neglecting to systematically match these problems with technological solutions and transform them into potential product features. This study aims to propose a sentiment and semantic analysis-based approach that correlates problems derived from customer feedback with patent-based solutions. The proposed approach utilizes Aspect-Based Sentiment Analysis to identify unmet needs from customer feedback, the BERTopic algorithm to extract solution-oriented themes from patent documents, and short text semantic similarity methods to associate problem-solution pairs. The applicability of the approach is demonstrated using 476 customer product reviews and 3548 patents in the Heating, Ventilation, and Air Conditioning (HVAC) field. The results show that customer-expressed problems can be semantically correlated with patent-based technological solutions, and these matches contribute to the identification of potential product features. The resulting problem-solution matches are structured along technological development horizons and presented as a technology roadmap output. The proposed approach offers a framework supporting systematic problem–solution matching based on sentiment and semantic analysis in technology-intensive sectors with large volumes of unstructured text data. 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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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 951
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 1917
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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24 pages, 1630 KB  
Systematic Review
Fake News Detection: It’s All in the Data!
by Soveatin Kuntur, Anna Wróblewska, Maria Ganzha, Marcin Paprzycki and Shelly Sachdeva
Appl. Sci. 2026, 16(3), 1585; https://doi.org/10.3390/app16031585 - 4 Feb 2026
Cited by 2 | Viewed by 4598
Abstract
This brief survey acts as a fundamental resource for researchers beginning their exploration into fake news detection. It emphasizes the importance of dataset quality and diversity in enhancing the effectiveness of detection models, detailing key features, labeling systems, and prevalent biases. It also [...] Read more.
This brief survey acts as a fundamental resource for researchers beginning their exploration into fake news detection. It emphasizes the importance of dataset quality and diversity in enhancing the effectiveness of detection models, detailing key features, labeling systems, and prevalent biases. It also presents the challenges and limitations. By addressing ethical considerations (such as privacy and consent, societal impacts, transparency, and accountability) and best practices (annotation methodologies, real-world dynamics, reliability, and validity), we offer a thorough overview of current datasets. Additionally, our contribution includes a GitHub repository that aggregates publicly available datasets into a single, easily accessible portal, thereby supporting further research and development in the fight against fake news. 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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