Big Text Data Analysis for Sentiment Classification in Product Reviews Using Advanced Large Language Models
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I2P107Keywords:
Sentiment Analysis, Product Reviews, BERT Model, Text Preprocessing, Machine Learning ClassificationAbstract
Sentiment analysis is the practice of mining data, opinions, reviews, or statements using natural language processing (NLP) to predict the statement's emotion. Sentiment analysis involves categorizing content into three stages: "positive," "negative," and "neutral." It has an impact on a large number of individuals and companies globally. Sentiment analysis is an essential task in natural language processing, particularly in the e-commerce sector where understanding customer sentiment may significantly influence company decisions. Using the dataset of Amazon product evaluations, it examines the application of Bidirectional Encoder Representations from Transformers (BERT) in deep learning-based sentiment classification in this paper. Standard text normalization techniques, including tokenization, case folding, and stop word removal, are applied to the dataset. BERT is used to set a performance benchmark to compare with two baseline models. A TF-IDF vector served as the feature representation for both methods. However, an approach based on the SentiWordNet lexicon was employed for the lexicon-based method. The models are compared using evaluation metrics such as F1 score, recall, accuracy, and precision. According to the experimental results, the proposed BERT model outperforms traditional methods in terms of F1-score (88.97%), recall (89.67%), accuracy (89.84%), and precision (88.87%). According to the research, transformer-based models outperform large-scale review datasets in sentiment analysis tasks by efficiently learning contextual knowledge and categorization
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