T5-Driven Neural Network for Optimized Sentiment Classification: Enhancing User Feedback Analysis with Deep Learning

T5-Driven Neural Network for Optimized Sentiment Classification: Enhancing User Feedback Analysis with Deep Learning | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 12 March 2025 V1 Latest version Share on T5-Driven Neural Network for Optimized Sentiment Classification: Enhancing User Feedback Analysis with Deep Learning Authors : Md. Faishal Ahmed Rudro [email protected] , Md. Shahriar Rahman Bhuiyan , and Afiah Authors Info & Affiliations https://doi.org/10.22541/au.174175572.29968179/v1 288 views 112 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Proper sentiment categorization is required to comprehend user opinions and optimize AI-based systems. Conventional machine learning techniques are usually hindered by the lack of context understanding and imbalance in classes, resulting in inferior predictions. The present work advocates the use of a T5-based sentence embedding technique for sentiment categorization wherein reviews are grouped into Positive, Neutral, and Negative classes based on review ratings. The preprocessing routine consists of text cleaning, tokenization, and stopword removal prior to proceeding with feature extraction from the T5 and reducing dimensions through Truncated SVD in order to decrease computation. Class imbalance was addressed through SMOTE-ENN balancing to determine strong generalizability of the models. Some various classifiers were utilized and cross-tested against the data set such as optimized neural networks, familiar classifiers like XGBoost, SVM, CatBoost, LightGBM, and Random Forest. The proposed deep neural network model produced the maximum validation accuracy of 97.01%, surpassing all the baseline models in precision (0.972), recall (0.970), F1-score (0.971), and AUC-ROC (0.992). Furthermore, a comparison with BERT, RoBERTa, and DistilBERT proved that T5 embeddings coupled with an optimized neural network gave better classification accuracy without losing out on computation. These results demonstrate the power of using transformer-based feature extraction with deep learning optimizations for sentiment classification tasks. Supplementary Material File (optimized sentiment classification with t5 embeddings_ a deep learning approach for enhanced user feedback analysis.docx) Download 403.50 KB Information & Authors Information Version history V1 Version 1 12 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords class balancing deep neural networks feature engineering sentiment classification t5 sentence embeddings transformer models Authors Affiliations Md. Faishal Ahmed Rudro [email protected] East West University View all articles by this author Md. Shahriar Rahman Bhuiyan Ahsanullah University of Science and Technology View all articles by this author Afiah University of Asia Pacific View all articles by this author Metrics & Citations Metrics Article Usage 288 views 112 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Md. Faishal Ahmed Rudro, Md. Shahriar Rahman Bhuiyan, Afiah. T5-Driven Neural Network for Optimized Sentiment Classification: Enhancing User Feedback Analysis with Deep Learning. Authorea . 12 March 2025. DOI: https://doi.org/10.22541/au.174175572.29968179/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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