Deep Learning Strategies for Next-Gen Sentiment Analysis with Green AI Practices

Ferrerira Cavalcante dos Santos, Mariana Ketley (2024) Deep Learning Strategies for Next-Gen Sentiment Analysis with Green AI Practices. Masters thesis, Dublin, National College of Ireland.

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Abstract

As consumer interest in green products grows, analysing reviews effectively is crucial. This study investigates sentiment analysis using deep learning on the Amazon Fine Food Reviews dataset, which includes over 500,000 reviews. Utilising the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology, it assesses attitudes towards eco-friendly products with a custom lexicon of sustainability terms. The research applies VADER (Valence Aware Dictionary and sEntiment Reasoner), a rule-based model, and RoBERTa (Robustly Optimized BERT Approach), a fine-tuned transformer-based deep learning model. TF-IDF (Term Frequency-Inverse Document Frequency) vectorization and Logistic Regression are used, with optimization via Hyperparameter GridSearchCV. Performance is measured by precision, recall, and F1-score, respectively. Green AI (Artificial Intelligence) practices, including DistilBERT Tokenizer and Model Pruning, are employed to minimise environmental impact. The study offers insights into consumer perceptions, helping businesses formulate sustainable strategies.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Haque, Rejwanul UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence H Social Sciences > HF Commerce > Marketing > Consumer Behaviour G Geography. Anthropology. Recreation > GE Environmental Sciences > Environment
Divisions:	School of Computing > Master of Science in Artificial Intelligence for Business
Depositing User:	Ciara O'Brien
Date Deposited:	02 Jul 2025 14:21
Last Modified:	02 Jul 2025 14:21
URI:	https://norma.ncirl.ie/id/eprint/7984

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