Article
Computer Science, Artificial Intelligence
Yang Zhao, Tommy W. S. Chow
Summary: With the prosperity of online social media, there has been a significant increase in user-generated reviews. This paper introduces a sentiment subset selection framework to filter irrelevant sentiment information and select subsets based on topic modeling and submodular maximization with a cardinality constraint. Empirical analysis shows that the proposed framework can compress sentiment corpus while maintaining classifier performance on different metrics.
NEURAL COMPUTING & APPLICATIONS
(2021)
Review
Computer Science, Artificial Intelligence
Tamara Amjad Al-Qablan, Mohd Halim Mohd Noor, Mohammed Azmi Al-Betar, Ahamad Tajudin Khader
Summary: Analyzing and understanding sentiments in social media documents is crucial for gaining insights into user opinions and overall perspectives on platforms like Twitter, Facebook, and Instagram. This paper provides an intensive review of sentiment analysis concepts and techniques, compares their performances, explores their applications, and discusses limitations and future directions. Researchers have utilized lexicon/rules, machine learning, and deep learning approaches for sentiment analysis. Performance ranges from 55-85% for lexicon/rules-based models, 55-90% for machine learning models, and 70-95% for deep learning models, reflecting variations based on factors such as dataset quality, model architecture, preprocessing techniques, and lexicon quality and coverage. Hybrid models and optimization techniques have been explored to further enhance performance.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Mohammed Hadwan, Mohammed Al-Sarem, Faisal Saeed, Mohammed A. Al-Hagery
Summary: Analyzing the sentiment of Arabic texts is a significant research challenge. Existing studies on Arabic sentiment analysis have focused on Twitter data while neglecting the reviews on Google Play or the App Store. This paper aims to analyze user opinions of six healthcare applications and proposes an improved sentiment classification approach using machine learning models.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Praphula Kumar Jain, Arjav Patel, Saru Kumari, Rajendra Pamula
Summary: This research analyzes passenger reviews and ratings for airlines to predict customer recommendations. The study observes a relationship between sentiments expressed in reviews and predictive consumer decisions. The findings contribute to service evaluation, policy-making, and predictive consumer recommendations.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Psychology, Multidisciplinary
Jingfang Liu, Mengshi Shi
Summary: This study uses machine learning technology to detect users with depression by analyzing user-shared content and posting behaviors. A hybrid feature selection and stacking ensemble strategy is proposed to improve the recognition accuracy. The experimental results show that this method achieves a high accuracy of 90.27% in identifying online patients.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
L. D. C. S. Subhashini, Yuefeng Li, Jinglan Zhang, Ajantha S. Atukorale, Yutong Wu
Summary: With the increasing number of customer reviews on the Web, there is a growing demand for effective methods to retrieve valuable information from reviews. Researchers have proposed many automatic mining and classification methods, but choosing a trusted method remains a challenge for companies. This article surveys recent opinion mining literature, focusing on text feature extraction, knowledge representation, and classification methods.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Review
Computer Science, Information Systems
Praphula Kumar Jain, Rajendra Pamula, Gautam Srivastava
Summary: Consumer sentiment analysis is a recent trend in social media-related applications, and machine learning techniques have been utilized to address the challenge of processing a large amount of social web data. This paper presents a systematic literature review to explore the future research directions and implications for service providers and researchers in the domain of hospitality and tourism.
COMPUTER SCIENCE REVIEW
(2021)
Article
Computer Science, Hardware & Architecture
Praphula Kumar Jain, Ephrem Admasu Yekun, Rajendra Pamula, Gautam Srivastava
Summary: Digital technology and social media offer many advantages in understanding human psychology, particularly for industrial growth. In online reviews, consumer recommendations are critical for service providers to enhance consumer policies and service quality.
COMPUTERS & ELECTRICAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Jinyang Du, Yin Zhang, Xiao Ma, Haoyu Wen, Giancarlo Fortino
Summary: By proposing a deep-level semi-self-help sentiment annotation system based on BERT and a novel classification model, the accuracy of ABSA tasks can be improved, space-time complexity can be reduced, and the amount of data annotation engineering required can be significantly decreased.
COGNITIVE COMPUTATION
(2021)
Article
Green & Sustainable Science & Technology
Mohamed Elhag Mohamed Abo, Norisma Idris, Rohana Mahmud, Atika Qazi, Ibrahim Abaker Targio Hashem, Jaafar Zubairu Maitama, Usman Naseem, Shah Khalid Khan, Shuiqing Yang
Summary: This study introduced a multi-criteria method to assess and rank classifiers for Arabic sentiment analysis, with deep learning and support vector machine (SVM) classifiers outperforming decision trees, K-nearest neighbours (K-NN), and Naive Bayes classifiers.
Article
Mathematics
Ismail Badache, Adrian-Gabriel Chifu, Sebastien Fournier
Summary: The paper focuses on estimating contradiction intensity in online courses by analyzing sentiment polarity and review ratings. Features like standard deviation of ratings, standard deviation of polarities, and number of reviews are found to be suitable for predicting contradiction intensity. Among supervised methods, the J48 decision trees algorithm performs the best in comparison.
Article
Computer Science, Information Systems
Mujahed Abdulqader, Abdallah Namoun, Yazed Alsaawy
Summary: Online reviews have an impact on consumers' purchasing decisions, but detecting fake reviews automatically is still a complex problem. This research develops a unified model based on psychological theories to detect fake online reviews and validates its effectiveness through empirical analysis.
Article
Computer Science, Artificial Intelligence
Behrooz Noori
Summary: This article introduces a new framework for categorizing and predicting customer sentiments, and the decision tree algorithm was found to provide the best results among various machine learning algorithms used. The most important factors influencing the great customer experience were extracted using the decision tree algorithm. An interesting observation was made on the effect of the number of features on the performance of machine learning algorithms.
APPLIED ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Hina Tufail, M. Usman Ashraf, Khalid Alsubhi, Hani Moaiteq Aljahdali
Summary: The outbreak of Covid-19 has led to an increase in global online shopping, highlighting the significant impact of online reviews on businesses. In our research, we proposed a fake review detection model using text classification and machine learning techniques, which outperformed other state-of-the-art techniques with high accuracy.
Article
Business
Chia-Hsuan Chang, San-Yih Hwang, Ming-Lun Wu
Summary: High-quality sentiment lexicons are crucial for lexicon-based sentiment analysis, but most lexicons are only available in certain dominant languages, limiting their applicability in specific domains or languages. This paper proposes a multistep approach for bilingual sentiment lexicon induction to disambiguate words with opposite sentiment polarities, which outperforms existing lexicons and competing approaches in terms of accuracy and coverage, using experiments on real-world online review data sets.
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
(2021)
