Elçin Alaçam^1,*, Mualla Yılmaz²

International Journal of Mental Health Promotion, Vol.27, No.12, pp. 1989-2005, 2025, DOI:10.32604/ijmhp.2025.071033 - 31 December 2025

Abstract Background: Post-cesarean surgical site infection is a frequent complication with significant consequences for maternal, physical, and psychological well-being. This study explored women’s lived experiences of post-cesarean surgical site infection, focusing on emotional responses, treatment experiences, and perceived psychosocial impact. Methods: A qualitative study was conducted using thematic analysis of semi-structured interviews with 23 patients hospitalized due to post-cesarean surgical site infection, selected through purposeful sampling between 15 August 2022, and 15 January 2024. Results: The mean age of the participants in the study was 28.69 ± 5.07 years. Of them, 13 were high school graduates, and 22… More >

Lan Luo¹, Yun Shen¹, Zuntao Gu², Linbing Wang¹, Shijian Sun^3,*

Journal of Psychology in Africa, Vol.35, No.5, pp. 713-721, 2025, DOI:10.32604/jpa.2025.066529 - 24 October 2025

Abstract This cross-sectional study examined how parenting styles influence college students’ romantic attitudes through para-social relationships. A total of 571 college students (females = 71.2%) completed the Short-form Parenting Style Scale, the Para-social Relationship Scale, and the Questionnaire on College Students’ View of Marriage and Love. Mediation was examined with bootstrap-corrected structural equation modelling (5000 resamples). Results indicated that maternal rejection (indirect effect β = −0.019, 95% CI = −0.055–−0.001, p < 0.05) and overprotection (indirect effect β = −0.02, 95% CI = −0.055–−0.001, p < 0.05) indirectly undermined college students’ marriage views by intensifying para-social relationships, whereas More >

Baishun Zhou^1,2, Haijiao Zhao³, Yuxin Wen², Gangyi Ding¹, Ying Xing^3,*, Xinyang Lin⁴, Lei Xiao⁵

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5201-5221, 2025, DOI:10.32604/cmc.2025.065726 - 30 July 2025

Abstract In recent years, with the rapid development of software systems, the continuous expansion of software scale and the increasing complexity of systems have led to the emergence of a growing number of software metrics. Defect prediction methods based on software metric elements highly rely on software metric data. However, redundant software metric data is not conducive to efficient defect prediction, posing severe challenges to current software defect prediction tasks. To address these issues, this paper focuses on the rational clustering of software metric data. Firstly, multiple software projects are evaluated to determine the preset number… More >

Yasser Alharbi¹, Sarwar Shah Khan^2,*

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 >

Farah Mohammad^1,2,*, Saad Al-Ahmadi³, Jalal Al-Muhtadi^1,3

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 >

Konstantinos I. Roumeliotis^1,*, 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 >

Buchi Reddy Ramakantha Reddy, Ramasamy Lokesh Kumar^*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4081-4107, 2024, DOI:10.32604/cmc.2024.057071 - 19 December 2024

Abstract Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products, leading to suboptimal user experiences. To address this, our study presents a Personalized Adaptive Multi-Product Recommendation System (PAMR) leveraging transfer learning and Bi-GRU (Bidirectional Gated Recurrent Units). Using a large dataset of user reviews from Amazon and Flipkart, we employ transfer learning with pre-trained models (AlexNet, GoogleNet, ResNet-50) to extract high-level attributes from product data, ensuring effective feature representation even with limited data. Bi-GRU captures both spatial and sequential dependencies in user-item interactions. The innovation of this study lies… More >

Longgang Zhao¹, Seok-Won Lee^2,*

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 >

Zohaib Ahmad Khan¹, Yuanqing Xia^1,*, Ahmed Khan², Muhammad Sadiq², Mahmood Alam³, Fuad A. Awwad⁴, Emad A. A. Ismail⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2771-2793, 2024, DOI:10.32604/cmc.2024.046897 - 15 May 2024

Abstract Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significant source of user-generated content. The development of sentiment lexicons that can support languages other than English is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existing sentiment analysis systems focus on English, leaving a significant research gap in other languages due to limited resources and tools. This research aims to address this gap by building a sentiment lexicon for local languages, which is then used with a machine learning algorithm for efficient sentiment analysis.… More >

Mian Muhammad Danyal¹, Muhammad Haseeb¹, Sarwar Shah Khan^2,*, Bilal Khan¹, Subhan Ullah¹

Journal on Artificial Intelligence, Vol.6, pp. 23-42, 2024, DOI:10.32604/jai.2023.045617 - 31 January 2024

Abstract Movies reviews provide valuable insights that can help people decide which movies are worth watching and avoid wasting their time on movies they will not enjoy. Movie reviews may contain spoilers or reveal significant plot details, which can reduce the enjoyment of the movie for those who have not watched it yet. Additionally, the abundance of reviews may make it difficult for people to read them all at once, classifying all of the movie reviews will help in making this decision without wasting time reading them all. Opinion mining, also called sentiment analysis, is the… More >