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  • 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

    Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering: An Innovative Multilingual Approach for Social Media Reviews

    Zohaib Ahmad Khan1, Yuanqing Xia1,*, 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 >

  • Open Access

    ARTICLE

    Opinion Mining on Movie Reviews Based on Deep Learning Models

    Mian Muhammad Danyal1, Muhammad Haseeb1, Sarwar Shah Khan2,*, 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 >

  • Open Access

    ARTICLE

    Sentiment Analysis Based on Performance of Linear Support Vector Machine and Multinomial Naïve Bayes Using Movie Reviews with Baseline Techniques

    Mian Muhammad Danyal1, Sarwar Shah Khan2,4, Muzammil Khan2,*, Muhammad Bilal Ghaffar1, Bilal Khan1, Muhammad Arshad3

    Journal on Big Data, Vol.5, pp. 1-18, 2023, DOI:10.32604/jbd.2023.041319 - 29 September 2023

    Abstract Movies are the better source of entertainment. Every year, a great percentage of movies are released. People comment on movies in the form of reviews after watching them. Since it is difficult to read all of the reviews for a movie, summarizing all of the reviews will help make this decision without wasting time in reading all of the reviews. Opinion mining also known as sentiment analysis is the process of extracting subjective information from textual data. Opinion mining involves identifying and extracting the opinions of individuals, which can be positive, neutral, or negative. The… More >

  • Open Access

    ARTICLE

    Medical Students’ Views on Psychiatry in Germany and Italy: Survey

    Andy Man Yeung Tai1,*, Janet Suen2, Mostafa Mamdouh Kamel2, Georg Schomerus3, Angelo Giovanni Icro Maremmaniz4, Reinhard Michael Krausz1

    International Journal of Mental Health Promotion, Vol.25, No.9, pp. 985-993, 2023, DOI:10.32604/ijmhp.2023.030087 - 10 August 2023

    Abstract Objectives: In 2019, the Insititue for Health Metrics and Evaluation reported that 16% of life lost were attributed to mental health. As a result, global shortage of psychiatrists is a pressing issue due to the increasing burden of mental illness. In 2016, a mere 5% of US medical students chose psychiatry as a career, a trend mirrored in Germany and Italy. As the medical students of 2016 have graduated or transitioned into residency in 2023, their attitudes towards psychiatry could have contributed to today’s shortage of psychiatrists. The global mental health burden has only been… More >

  • Open Access

    ARTICLE

    An Ensemble-Based Hotel Reviews System Using Naive Bayes Classifier

    Joseph Bamidele Awotunde1, Sanjay Misra2,*, Vikash Katta2, Oluwafemi Charles Adebayo1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 131-154, 2023, DOI:10.32604/cmes.2023.026812 - 23 April 2023

    Abstract The task of classifying opinions conveyed in any form of text online is referred to as sentiment analysis. The emergence of social media usage and its spread has given room for sentiment analysis in our daily lives. Social media applications and websites have become the foremost spring of data recycled for reviews for sentimentality in various fields. Various subject matter can be encountered on social media platforms, such as movie product reviews, consumer opinions, and testimonies, among others, which can be used for sentiment analysis. The rapid uncovering of these web contents contains divergence of… More >

  • Open Access

    ARTICLE

    Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

    Ibrahim M. Alwayle1, Badriyya B. Al-onazi2, Mohamed K. Nour3, Khaled M. Alalayah1, Khadija M. Alaidarous1, Ibrahim Abdulrab Ahmed4, Amal S. Mehanna5, Abdelwahed Motwakel6,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2947-2961, 2023, DOI:10.32604/csse.2023.034456 - 03 April 2023

    Abstract Online reviews regarding purchasing services or products offered are the main source of users’ opinions. To gain fame or profit, generally, spam reviews are written to demote or promote certain targeted products or services. This practice is called review spamming. During the last few years, various techniques have been recommended to solve the problem of spam reviews. Previous spam detection study focuses on English reviews, with a lesser interest in other languages. Spam review detection in Arabic online sources is an innovative topic despite the vast amount of data produced. Thus, this study develops an… More >

  • Open Access

    ARTICLE

    Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

    Muhammad Rizwan Rashid Rana1,*, Saif Ur Rehman1, Asif Nawaz1, Tariq Ali1, Azhar Imran2, Abdulkareem Alzahrani3, Abdullah Almuhaimeed4,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2415-2428, 2023, DOI:10.32604/csse.2023.035149 - 09 February 2023

    Abstract People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events, public products and the latest affairs. People share their thoughts and feelings about various topics, including products, news, blogs, etc. In user reviews and tweets, sentiment analysis is used to discover opinions and feelings. Sentiment polarity is a term used to describe how sentiment is represented. Positive, neutral and negative are all examples of it. This area is still in its infancy and needs several critical upgrades. Slang and hidden emotions can detract from the accuracy of traditional… More >

  • Open Access

    ARTICLE

    Topic Modelling and Sentimental Analysis of Students’ Reviews

    Omer S. Alkhnbashi1, Rasheed Mohammad Nassr2,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6835-6848, 2023, DOI:10.32604/cmc.2023.034987 - 28 December 2022

    Abstract Globally, educational institutions have reported a dramatic shift to online learning in an effort to contain the COVID-19 pandemic. The fundamental concern has been the continuance of education. As a result, several novel solutions have been developed to address technical and pedagogical issues. However, these were not the only difficulties that students faced. The implemented solutions involved the operation of the educational process with less regard for students’ changing circumstances, which obliged them to study from home. Students should be asked to provide a full list of their concerns. As a result, student reflections, including… More >

  • Open Access

    ARTICLE

    Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

    Ernesto Lee1, Furqan Rustam2, Hina Fatima Shahzad2, Patrick Bernard Washington3, Abid Ishaq3, Imran Ashraf4,*

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 3053-3077, 2023, DOI:10.32604/csse.2023.029059 - 21 December 2022

    Abstract With the increasing usage of drugs to remedy different diseases, drug safety has become crucial over the past few years. Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae. Such diversification is both helpful and dangerous as such medicine proves to be more effective or shows side effects to different patients. Despite clinical trials, side effects are reported when the medicine is used by the mass public, of which several such experiences are shared on social media platforms. A system capable of analyzing such… More >

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