Comprehensive Exploration of Machine and Deep Learning Classification Methods for Aspect-Based Sentiment Analysis with Latent Dirichlet Allocation Topic Modeling
DOI:
https://doi.org/10.62411/faith.2024-3Keywords:
Aspect-Based Sentiment Analysis, Aspect Extraction, Natural Language Processing, Product Reviews Sentiment, Sentiment Model EvaluationAbstract
This research explores the effectiveness of machine learning (ML) and deep learning (DL) classification methods in Aspect-Based Sentiment Analysis (ABSA) on product reviews, incorporating Latent Dirichlet Allocation (LDA) for topic modeling. Using the Amazon reviews dataset, this research tests models such as Naive Bayes (NB), Support Vector Machines (SVM), Random Forest (RF), Long Short-Term Memory (LSTM), and Gated Recurrent Units(GRU). Important aspects such as the product's quality, practicality, and reliability are discussed. The results show that the RF and DL models provide competitive performance, with the RF achieving an accuracy of up to 94.50% and an F1 score of 95.45% for the reliability aspect. The study's conclusions emphasize the importance of selecting an appropriate model based on specifications and data requirements for ABSA, as well as recognizing the need to strike a balance between accuracy and computational efficiency.
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