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dc.contributor.authorAli, Hashir
dc.contributor.authorHashmi, Ehtesham
dc.contributor.authorYildirim-Yayilgan, Sule
dc.contributor.authorShaikh, Sarang
dc.date.accessioned2024-04-08T11:19:58Z
dc.date.available2024-04-08T11:19:58Z
dc.date.created2024-03-31T16:58:56Z
dc.date.issued2024
dc.identifier.issn2079-9292
dc.identifier.urihttps://hdl.handle.net/11250/3125268
dc.description.abstractIn recent years, online shopping has surged in popularity, with customer reviews becoming a crucial aspect of the decision-making process. Reviews not only help potential customers make informed choices, but also provide businesses with valuable feedback and build trust. In this study, we conducted a thorough analysis of the Amazon reviews dataset, which includes several product categories. Our primary objective was to accurately classify sentiments using natural language processing, machine learning, ensemble learning, and deep learning techniques. Our research workflow encompassed several crucial steps. We explore data collection procedures; preprocessing steps, including normalization and tokenization; and feature extraction, utilizing the Bag-of-Words and TF–IDF methods. We conducted experiments employing a variety of machine learning algorithms, including Multinomial Naive Bayes, Random Forest, Decision Tree, and Logistic Regression. Additionally, we harnessed Bagging as an ensemble learning technique. Furthermore, we explored deep learning-based algorithms, such as CNNs, Bidirectional LSTM, and transformer-based models, like XLNet and BERT. Our comprehensive evaluations, utilizing metrics such as accuracy, precision, recall, and F1 score, revealed that the BERT algorithm outperformed others, achieving an impressive accuracy rate of 89%. This research provides valuable insights into the sentiment analysis of Amazon reviews, aiding both consumers and businesses in making informed decisions and enhancing product and service quality.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleAnalyzing Amazon Products Sentiment: A Comparative Study of Machine and Deep Learning, and Transformer-Based Techniquesen_US
dc.title.alternativeAnalyzing Amazon Products Sentiment: A Comparative Study of Machine and Deep Learning, and Transformer-Based Techniquesen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.source.volume13en_US
dc.source.journalElectronicsen_US
dc.source.issue7en_US
dc.identifier.doi10.3390/electronics13071305
dc.identifier.cristin2257710
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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