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  • Open Access

    ARTICLE

    AI-Driven Sentiment Analysis: Understanding Customer Feedbacks on Women’s Clothing through CNN and LSTM

    Phan-Anh-Huy Nguyen*, Luu-Luyen Than

    Intelligent Automation & Soft Computing, Vol.40, pp. 221-234, 2025, DOI:10.32604/iasc.2025.058976 - 14 April 2025

    Abstract The burgeoning e-commerce industry has made online customer reviews a crucial source of feedback for businesses. Sentiment analysis, a technique used to extract subjective information from text, has become essential for understanding consumer sentiment and preferences. However, traditional sentiment analysis methods often struggle with the nuances and context of natural language. To address these issues, this study proposes a comparison of deep learning models that figure out the optimal method to accurately analyze consumer reviews on women's clothing. CNNs excel at capturing local features and semantic information, while LSTMs are adept at handling long-range dependencies… More >

  • Open Access

    ARTICLE

    Classifying Multi-Lingual Reviews Sentiment Analysis in Arabic and English Languages Using the Stochastic Gradient Descent Model

    Yasser Alharbi1, Sarwar Shah Khan2,*

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1275-1290, 2025, DOI:10.32604/cmc.2025.061490 - 26 March 2025

    Abstract Sentiment analysis plays an important role in distilling and clarifying content from movie reviews, aiding the audience in understanding universal views towards the movie. However, the abundance of reviews and the risk of encountering spoilers pose challenges for efficient sentiment analysis, particularly in Arabic content. This study proposed a Stochastic Gradient Descent (SGD) machine learning (ML) model tailored for sentiment analysis in Arabic and English movie reviews. SGD allows for flexible model complexity adjustments, which can adapt well to the Involvement of Arabic language data. This adaptability ensures that the model can capture the nuances… More >

  • Open Access

    ARTICLE

    SESDP: A Sentiment Analysis-Driven Approach for Enhancing Software Product Security by Identifying Defects through Social Media Reviews

    Farah Mohammad1,2,*, Saad Al-Ahmadi3, Jalal Al-Muhtadi1,3

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1327-1345, 2025, DOI:10.32604/cmc.2025.060228 - 26 March 2025

    Abstract Software defect prediction is a critical component in maintaining software quality, enabling early identification and resolution of issues that could lead to system failures and significant financial losses. With the increasing reliance on user-generated content, social media reviews have emerged as a valuable source of real-time feedback, offering insights into potential software defects that traditional testing methods may overlook. However, existing models face challenges like handling imbalanced data, high computational complexity, and insufficient integration of contextual information from these reviews. To overcome these limitations, this paper introduces the SESDP (Sentiment Analysis-Based Early Software Defect Prediction)… More >

  • Open Access

    ARTICLE

    X-OODM: Leveraging Explainable Object-Oriented Design Methodology for Multi-Domain Sentiment Analysis

    Abqa Javed1, Muhammad Shoaib1,*, Abdul Jaleel2, Mohamed Deriche3, Sharjeel Nawaz4

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4977-4994, 2025, DOI:10.32604/cmc.2025.057359 - 06 March 2025

    Abstract Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability, transparency, and trust in the community. Multi-domain Sentiment Analysis is a significant web-based system where the explainability feature is essential for achieving user satisfaction. Conventional design methodologies such as object-oriented design methodology (OODM) have been proposed for web-based application development, which facilitates code reuse, quantification, and security at the design level. However, OODM did not provide the feature of explainability in web-based decision-making systems. X-OODM modifies the OODM with added explainable models to introduce the explainability feature for… More >

  • Open Access

    ARTICLE

    Optimizing Airline Review Sentiment Analysis: A Comparative Analysis of LLaMA and BERT Models through Fine-Tuning and Few-Shot Learning

    Konstantinos I. Roumeliotis1,*, Nikolaos D. Tselikas2, Dimitrios K. Nasiopoulos3

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2769-2792, 2025, DOI:10.32604/cmc.2025.059567 - 17 February 2025

    Abstract In the rapidly evolving landscape of natural language processing (NLP) and sentiment analysis, improving the accuracy and efficiency of sentiment classification models is crucial. This paper investigates the performance of two advanced models, the Large Language Model (LLM) LLaMA model and NLP BERT model, in the context of airline review sentiment analysis. Through fine-tuning, domain adaptation, and the application of few-shot learning, the study addresses the subtleties of sentiment expressions in airline-related text data. Employing predictive modeling and comparative analysis, the research evaluates the effectiveness of Large Language Model Meta AI (LLaMA) and Bidirectional Encoder… More >

  • Open Access

    ARTICLE

    External Knowledge-Enhanced Cross-Attention Fusion Model for Tobacco Sentiment Analysis

    Lihua Xie1, Ni Tang1, Qing Chen1,*, Jun Li2,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3381-3397, 2025, DOI:10.32604/cmc.2024.058950 - 17 February 2025

    Abstract In the age of information explosion and artificial intelligence, sentiment analysis tailored for the tobacco industry has emerged as a pivotal avenue for cigarette manufacturers to enhance their tobacco products. Existing solutions have primarily focused on intrinsic features within consumer reviews and achieved significant progress through deep feature extraction models. However, they still face these two key limitations: (1) neglecting the influence of fundamental tobacco information on analyzing the sentiment inclination of consumer reviews, resulting in a lack of consistent sentiment assessment criteria across thousands of tobacco brands; (2) overlooking the syntactic dependencies between Chinese… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning Approach for Automating App Review Classification: Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

    Nahed Alsaleh1,2, Reem Alnanih1,*, Nahed Alowidi1

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 949-976, 2025, DOI:10.32604/cmc.2024.059351 - 03 January 2025

    Abstract App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products. Automating the analysis of these reviews is vital for efficient review management. While traditional machine learning (ML) models rely on basic word-based feature extraction, deep learning (DL) methods, enhanced with advanced word embeddings, have shown superior performance. This research introduces a novel aspect-based sentiment analysis (ABSA) framework to classify app reviews based on key non-functional requirements, focusing on usability factors: effectiveness, efficiency, and satisfaction. We propose a hybrid DL model, combining BERT (Bidirectional Encoder Representations from Transformers) More >

  • Open Access

    ARTICLE

    Text-Image Feature Fine-Grained Learning for Joint Multimodal Aspect-Based Sentiment Analysis

    Tianzhi Zhang1, Gang Zhou1,*, Shuang Zhang2, Shunhang Li1, Yepeng Sun1, Qiankun Pi1, Shuo Liu3

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 279-305, 2025, DOI:10.32604/cmc.2024.055943 - 03 January 2025

    Abstract Joint Multimodal Aspect-based Sentiment Analysis (JMASA) is a significant task in the research of multimodal fine-grained sentiment analysis, which combines two subtasks: Multimodal Aspect Term Extraction (MATE) and Multimodal Aspect-oriented Sentiment Classification (MASC). Currently, most existing models for JMASA only perform text and image feature encoding from a basic level, but often neglect the in-depth analysis of unimodal intrinsic features, which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features. Given this problem, we propose a Text-Image Feature Fine-grained… More >

  • Open Access

    ARTICLE

    Integrating Ontology-Based Approaches with Deep Learning Models for Fine-Grained Sentiment Analysis

    Longgang Zhao1, Seok-Won Lee2,*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1855-1877, 2024, DOI:10.32604/cmc.2024.056215 - 15 October 2024

    Abstract Although sentiment analysis is pivotal to understanding user preferences, existing models face significant challenges in handling context-dependent sentiments, sarcasm, and nuanced emotions. This study addresses these challenges by integrating ontology-based methods with deep learning models, thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant feedback. The framework comprises explicit topic recognition, followed by implicit topic identification to mitigate topic interference in subsequent sentiment analysis. In the context of sentiment analysis, we develop an expanded sentiment lexicon based on domain-specific corpora by leveraging techniques such as word-frequency analysis and word embedding. More >

  • Open Access

    ARTICLE

    Cross-Target Stance Detection with Sentiments-Aware Hierarchical Attention Network

    Kelan Ren, Facheng Yan, Honghua Chen, Wen Jiang, Bin Wei, Mingshu Zhang*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 789-807, 2024, DOI:10.32604/cmc.2024.055624 - 15 October 2024

    Abstract The task of cross-target stance detection faces significant challenges due to the lack of additional background information in emerging knowledge domains and the colloquial nature of language patterns. Traditional stance detection methods often struggle with understanding limited context and have insufficient generalization across diverse sentiments and semantic structures. This paper focuses on effectively mining and utilizing sentiment-semantics knowledge for stance knowledge transfer and proposes a sentiment-aware hierarchical attention network (SentiHAN) for cross-target stance detection. SentiHAN introduces an improved hierarchical attention network designed to maximize the use of high-level representations of targets and texts at various… More > Graphic Abstract

    Cross-Target Stance Detection with Sentiments-Aware Hierarchical Attention Network

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