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Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis

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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 March 2025 | Viewed by 12560

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Special Issue Editors

Dr. Silvia García-Méndez

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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

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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

Dr. Francisco De Arriba-Pérez

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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 necessity to develop 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" welcomes contributions to these advanced techniques with particular attention 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, 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

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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 (10 papers)

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Research

32 pages, 777 KiB

Open AccessArticle

A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data

by Jothi Prakash Venugopal, Arul Antran Vijay Subramanian, Gopikrishnan Sundaram, Marco Rivera and Patrick Wheeler

Appl. Sci. 2024, 14(23), 11471; https://doi.org/10.3390/app142311471 - 9 Dec 2024

Viewed by 619

Abstract

Sentiment analysis is a vital component of natural language processing (NLP), enabling the classification of text into positive, negative, or neutral sentiments. It is widely used in customer feedback analysis and social media monitoring but faces a significant challenge: bias. Biases, often introduced through imbalanced training datasets, can distort model predictions and result in unfair outcomes. To address this, we propose a bias-aware sentiment analysis framework leveraging Bias-BERT (Bidirectional Encoder Representations from Transformers), a customized classifier designed to balance accuracy and fairness. Our approach begins with adapting the Jigsaw Unintended Bias in Toxicity Classification dataset by converting toxicity scores into sentiment labels, making it suitable for sentiment analysis. This process includes data preparation steps like cleaning, tokenization, and feature extraction, all aimed at reducing bias. At the heart of our method is a novel loss function incorporating a bias-aware term based on the Kullback–Leibler (KL) divergence. This term guides the model toward fair predictions by penalizing biased outputs while maintaining robust classification performance. Ethical considerations are integral to our framework, ensuring the responsible deployment of AI models. This methodology highlights a pathway to equitable sentiment analysis by actively mitigating dataset biases and promoting fairness in NLP applications. Full article

(This article belongs to the Special Issue Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis)

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27 pages, 5233 KiB

Open AccessArticle

A Sentiment Analysis Model Based on User Experiences of Dubrovnik on the Tripadvisor Platform

by Ivona Zakarija, Frano Škopljanac-Mačina, Hrvoje Marušić and Bruno Blašković

Appl. Sci. 2024, 14(18), 8304; https://doi.org/10.3390/app14188304 - 14 Sep 2024

Viewed by 870

Abstract

Emerging research indicates that sentiment analyses of Dubrovnik focus mainly on hotel accommodations and restaurants. However, little attention has been paid to attractions, even though they are an important aspect of destinations and require more care and investment than amenities. This study examines how visitors experience Dubrovnik based on the reviews published on the Tripadvisor platform. Data were collected by implementing a web-scraping script to retrieve reviews of the tourist attraction “Old Town” from Tripadvisor, while data augmentation and random oversampling techniques were applied to address class imbalances. A sentiment analysis model, based on the pre-trained RoBERTa, was also developed and evaluated. In particular, a sentiment analysis was performed to compare reviews from 2022 and 2023. Overall, the results of this study are promising and demonstrate the effectiveness of this model and its potential applicability to other attractions. These findings provide valuable insights for decision makers to improve services and to increase visitor engagement. Full article

(This article belongs to the Special Issue Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis)

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24 pages, 3162 KiB

Open AccessArticle

Detecting Offensive Language on Malay Social Media: A Zero-Shot, Cross-Language Transfer Approach Using Dual-Branch mBERT

by Xingyi Guo, Hamedi Mohd Adnan and Muhammad Zaiamri Zainal Abidin

Appl. Sci. 2024, 14(13), 5777; https://doi.org/10.3390/app14135777 - 2 Jul 2024

Cited by 1 | Viewed by 1021

Abstract

Social media serves as a platform for netizens to stay informed and express their opinions through the Internet. Currently, the social media discourse environment faces a significant security threat—offensive comments. A group of users posts comments that are provocative, discriminatory, and objectionable, intending to disrupt online discussions, provoke others, and incite intergroup conflict. These comments undermine citizens’ legitimate rights, disrupt social order, and may even lead to real-world violent incidents. However, current automatic detection of offensive language primarily focuses on a few high-resource languages, leaving low-resource languages, such as Malay, with insufficient annotated corpora for effective detection. To address this, we propose a zero-shot, cross-language unsupervised offensive language detection (OLD) method using a dual-branch mBERT transfer approach. Firstly, using the multi-language BERT (mBERT) model as the foundational language model, the first network branch automatically extracts features from both source and target domain data. Subsequently, Sinkhorn distance is employed to measure the discrepancy between the source and target language feature representations. By estimating the Sinkhorn distance between the labeled source language (e.g., English) and the unlabeled target language (e.g., Malay) feature representations, the method minimizes the Sinkhorn distance adversarially to provide more stable gradients, thereby extracting effective domain-shared features. Finally, offensive pivot words from the source and target language training sets are identified. These pivot words are then removed from the training data in a second network branch, which employs the same architecture. This process constructs an auxiliary OLD task. By concealing offensive pivot words in the training data, the model reduces overfitting and enhances robustness to the target language. In the end-to-end framework training, the combination of cross-lingual shared features and independent features culminates in unsupervised detection of offensive speech in the target language. The experimental results demonstrate that employing cross-language model transfer learning can achieve unsupervised detection of offensive content in low-resource languages. The number of labeled samples in the source language is positively correlated with transfer performance, and a greater similarity between the source and target languages leads to better transfer effects. The proposed method achieves the best performance in OLD on the Malay dataset, achieving an F1 score of 80.7%. It accurately identifies features of offensive speech, such as sarcasm, mockery, and implicit expressions, and showcases strong generalization and excellent stability across different target languages. Full article

(This article belongs to the Special Issue Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis)

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21 pages, 3471 KiB

Open AccessArticle

Adaptation of Augmentative and Alternative Communicators through the Study of Interactions with High-Tech Solution Users

by Jaime González-González, Enrique Costa-Montenegro, Fátima María García-Doval, Cristina López-Bravo and Francisco de Arriba-Pérez

Appl. Sci. 2024, 14(13), 5641; https://doi.org/10.3390/app14135641 - 28 Jun 2024

Viewed by 747

Abstract

Augmentative and Alternative Communication (aac) strategies ease communication tasks for people who require accessible solutions. These strategies are usually addressed by technological solutions such as mobile applications. This research seeks clues on the development of such applications by analyzing user interactions with Android application PictoDroid Lite, an aac communicator. This study considered a data set containing more than 85,000 interactions of users from more than 50 countries. The goal was to identify the primary needs reflected in the users’ behavior and how these applications handle them, providing other researchers and developers with relevant information about how users interact with these applications. We detected areas of improvement regarding the adaptation to users’ needs in terms of profiling, smart suggestions, and time habits. Full article

(This article belongs to the Special Issue Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis)

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19 pages, 6084 KiB

Open AccessArticle

Hate Speech Detection by Using Rationales for Judging Sarcasm

by Maliha Binte Mamun, Takashi Tsunakawa, Masafumi Nishida and Masafumi Nishimura

Appl. Sci. 2024, 14(11), 4898; https://doi.org/10.3390/app14114898 - 5 Jun 2024

Viewed by 1785

Abstract

The growing number of social media users has impacted the rise in hate comments and posts. While extensive research in hate speech detection attempts to combat this phenomenon by developing new datasets and detection models, reconciling classification accuracy with broader decision-making metrics like plausibility and faithfulness remains challenging. As restrictions on social media tighten to stop the spread of hate and offensive content, users have adapted by finding new approaches, often camouflaged in the form of sarcasm. Therefore, dealing with new trends such as the increased use of emoticons (negative emoticons in positive sentences) and sarcastic comments is necessary. This paper introduces sarcasm-based rationale (emoticons or portions of text that indicate sarcasm) combined with hate/offensive rationale for better detection of hidden hate comments/posts. A dataset was created by labeling texts and selecting rationale based on sarcasm from the existing benchmark hate dataset, HateXplain. The newly formed dataset was then applied in the existing state-of-the-art model. The model’s F1-score increased by 0.01 when using sarcasm rationale with hate/offensive rationale in a newly formed attention proposed in the data’s preprocessing step. Also, with the new data, a significant improvement was observed in explainability metrics such as plausibility and faithfulness. Full article

(This article belongs to the Special Issue Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis)

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23 pages, 971 KiB

Open AccessArticle

A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models

by Monserrat Vázquez-Hernández, Luis Alberto Morales-Rosales, Ignacio Algredo-Badillo, Sofía Isabel Fernández-Gregorio, Héctor Rodríguez-Rangel and María-Luisa Córdoba-Tlaxcalteco

Appl. Sci. 2024, 14(11), 4614; https://doi.org/10.3390/app14114614 - 27 May 2024

Cited by 1 | Viewed by 1364

Abstract

In recent years, the use of deep learning models for deploying sentiment analysis systems has become a widespread topic due to their processing capacity and superior results on large volumes of information. However, after several years’ research, previous works have demonstrated that deep learning models are vulnerable to strategically modified inputs called adversarial examples. Adversarial examples are generated by performing perturbations on data input that are imperceptible to humans but that can fool deep learning models’ understanding of the inputs and lead to false predictions being generated. In this work, we collect, select, summarize, discuss, and comprehensively analyze research works to generate textual adversarial examples. There are already a number of reviews in the existing literature concerning attacks on deep learning models for text applications; in contrast to previous works, however, we review works mainly oriented to sentiment analysis tasks. Further, we cover the related information concerning generation of adversarial examples to make this work self-contained. Finally, we draw on the reviewed literature to discuss adversarial example design in the context of sentiment analysis tasks. Full article

(This article belongs to the Special Issue Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis)

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13 pages, 509 KiB

Open AccessArticle

Knowledge Graph Completion Using a Pre-Trained Language Model Based on Categorical Information and Multi-Layer Residual Attention

by Qiang Rao, Tiejun Wang, Xiaoran Guo, Kaijie Wang and Yue Yan

Appl. Sci. 2024, 14(11), 4453; https://doi.org/10.3390/app14114453 - 23 May 2024

Viewed by 909

Abstract

Knowledge graph completion (KGC) utilizes known knowledge graph triples to infer and predict missing knowledge, making it one of the research hotspots in the field of knowledge graphs. There are still limitations in generating high-quality entity embeddings and fully understanding the contextual information of entities and relationships. To overcome these challenges, this paper introduces a novel pre-trained language model-based method for knowledge graph completion that significantly enhances the quality of entity embeddings by integrating entity categorical information with textual descriptions. Additionally, this method employs an innovative multi-layer residual attention network in combination with PLMs, deepening the understanding of the joint contextual information of entities and relationships. Experimental results on the FB15k-237 and WN18RR datasets demonstrate that our proposed model significantly outperforms existing baseline models in link prediction tasks. Full article

(This article belongs to the Special Issue Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis)

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18 pages, 996 KiB

Open AccessArticle

REACT: Relation Extraction Method Based on Entity Attention Network and Cascade Binary Tagging Framework

by Lingqi Kong and Shengquau Liu

Appl. Sci. 2024, 14(7), 2981; https://doi.org/10.3390/app14072981 - 2 Apr 2024

Cited by 1 | Viewed by 896

Abstract

With the development of the Internet, vast amounts of text information are being generated constantly. Methods for extracting the valuable parts from this information have become an important research field. Relation extraction aims to identify entities and the relations between them from text, helping computers better understand textual information. Currently, the field of relation extraction faces various challenges, particularly in addressing the relation overlapping problem. The main difficulties are as follows: (1) Traditional methods of relation extraction have limitations and lack the ability to handle the relation overlapping problem, requiring a redesign. (2) Relation extraction models are easily disturbed by noise from words with weak relevance to the relation extraction task, leading to difficulties in correctly identifying entities and their relations. In this paper, we propose the Relation extraction method based on the Entity Attention network and Cascade binary Tagging framework (REACT). We decompose the relation extraction task into two subtasks: head entity identification and tail entity and relation identification. REACT first identifies the head entity and then identifies all possible tail entities that can be paired with the head entity, as well as all possible relations. With this architecture, the model can handle the relation overlapping problem. In order to reduce the interference of words in the text that are not related to the head entity or relation extraction task and improve the accuracy of identifying the tail entities and relations, we designed an entity attention network. To demonstrate the effectiveness of REACT, we construct a high-quality Chinese dataset and conduct a large number of experiments on this dataset. The experimental results fully confirm the effectiveness of REACT, showing its significant advantages in handling the relation overlapping problem compared to current other methods. Full article

(This article belongs to the Special Issue Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis)

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15 pages, 557 KiB

Open AccessArticle

Prefix Data Augmentation for Contrastive Learning of Unsupervised Sentence Embedding

by Chunchun Wang and Shu Lv

Appl. Sci. 2024, 14(7), 2880; https://doi.org/10.3390/app14072880 - 29 Mar 2024

Viewed by 1212

Abstract

This paper presents prefix data augmentation (Prd) as an innovative method for enhancing sentence embedding learning through unsupervised contrastive learning. The framework, dubbed PrdSimCSE, uses Prd to create both positive and negative sample pairs. By appending positive and negative prefixes to a sentence, the basis for contrastive learning is formed, outperforming the baseline unsupervised SimCSE. PrdSimCSE is positioned within a probabilistic framework that expands the semantic similarity event space and generates superior negative samples, contributing to more accurate semantic similarity estimations. The model’s efficacy is validated on standard semantic similarity tasks, showing a notable improvement over that of existing unsupervised models, specifically a 1.08% enhancement in performance on BERTbase. Through detailed experiments, the effectiveness of positive and negative prefixes in data augmentation and their impact on the learning model are explored, and the broader implications of prefix data augmentation are discussed for unsupervised sentence embedding learning. Full article

(This article belongs to the Special Issue Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis)

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16 pages, 2169 KiB

Open AccessArticle

Causal Reinforcement Learning for Knowledge Graph Reasoning

by Dezhi Li, Yunjun Lu, Jianping Wu, Wenlu Zhou and Guangjun Zeng

Appl. Sci. 2024, 14(6), 2498; https://doi.org/10.3390/app14062498 - 15 Mar 2024

Cited by 1 | Viewed by 1775

Abstract

Knowledge graph reasoning can deduce new facts and relationships, which is an important research direction of knowledge graphs. Most of the existing methods are based on end-to-end reasoning which cannot effectively use the knowledge graph, so consequently the performance of the method still needs to be improved. Therefore, we combine causal inference with reinforcement learning and propose a new framework for knowledge graph reasoning. By combining the counterfactual method in causal inference, our method can obtain more information as prior knowledge and integrate it into the control strategy in the reinforcement model. The proposed method mainly includes the steps of relationship importance identification, reinforcement learning framework design, policy network design, and the training and testing of the causal reinforcement learning model. Specifically, a prior knowledge table is first constructed to indicate which relationship is more important for the problem to be queried; secondly, designing state space, optimization, action space, state transition and reward, respectively, is undertaken; then, the standard value is set and compared with the weight value of each candidate edge, and an action strategy is selected according to the comparison result through prior knowledge or neural network; finally, the parameters of the reinforcement learning model are determined through training and testing. We used four datasets to compare our method to the baseline method and conducted ablation experiments. On dataset NELL-995 and FB15k-237, the experimental results show that the MAP scores of our method are 87.8 and 45.2, and the optimal performance is achieved. Full article

(This article belongs to the Special Issue Advancements in Natural Language Processing, Semantic Networks, and Sentiment Analysis)

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