A Comprehensive Evaluation of Machine Learning and Transformer-Based Techniques for Sentiment Analysis

Emotions, as an essential part of text, play a significant role in opinion mining, enabling the judgment of users’ attitudes and the monitoring of public opinion. This study compares statistical learning methods, deep neural networks, and pre-trained models for sentiment polarity classification tasks using the Internet Movie Database (IMDB) movie review dataset, the ChnSentiCorp Chinese sentiment dataset, and financial data. Empirical evidence shows that traditional approaches, such as support vector machines—which go beyond classic algorithms—still demonstrate strong performance in classifying data. However, deep learning models based on bidirectional long short-term memory networks achieve considerable improvements in accuracy due to their ability to capture contextual information. More impressively, the Internet Movie Database (BERT) model achieves 100% on all evaluation metrics after knowledge distillation, further confirming the advantages of pre-training techniques for these datasets. These results provide insights into how different models evolve and highlight the need to balance model accuracy, computational cost, and data requirements in practical applications.