Optimizing Machine Learning & Deep Learning Models for Sentiment Classification of Online Product Reviews
Keywords:
Machne Learning, Deep Learning, LSTM, TF-IDF, BoW, Word2VecAbstract
The rapid expansion of e-commerce has led to an overwhelming amount of user-generated content in the form of online product reviews. Sentiment classification of these reviews is critical for businesses and consumers alike, enabling better decision-making and enhancing customer satisfaction. This paper explores various machine learning (ML) and deep learning (DL) techniques for optimizing sentiment classification models specifically tailored for online product reviews. The methodology integrates various NLP techniques like Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and Word2Vec embeddings. These were combined with machine learning models such as Multinomial Naive Bayes, Logistic Regression, Random Forest, and Long Short-Term Memory (LSTM) neural networks. We conduct a comparative analysis of traditional ML algorithms and deep learning architectures in terms of performance, efficiency, and interpretability. Various techniques such as feature engineering, hyperparameter tuning, and model optimization are evaluated. The results demonstrate that deep learning models, particularly transformer-based architectures, outperform conventional ML methods in terms of accuracy, robustness, and scalability, though ML models remain relevant due to their efficiency and interpretability
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