Efficient Three-Class Sentiment Classification of IMDb Reviews Using LoRA-Based Fine-Tuning on Pseudo-labeled Data
Sentiment analysis has become an important task in natural language processing for understanding public opinions expressed in online reviews. However, most publicly available IMDb datasets are limited to binary sentiment labels, which restricts the ability of sentiment analysis systems to capture neutral opinions. This study proposes an efficient sentiment analysis framework that transforms the binary IMDb dataset into a three-class sentiment classification problem consisting of positive, neutral, and negative sentiments. The proposed approach integrates pseudolabeling with Parameter-Efficient Fine-Tuning (PEFT) using the Low-Rank Adaptation (LoRA) technique on the Longformer architecture. Experimental results show that the model achieves an accuracy of 77.06%, a weighted F1-score of 72.17%, and a Matthews Correlation Coefficient (MCC) of 0.6232. The results demonstrate that LoRA-based fine-tuning can significantly reduce computational requirements while maintaining competitive performance in sentiment classification tasks. These findings indicate that the proposed framework provides a practical and computationally efficient solution for large-scale sentiment analysis, particularly for environments with limited computational resources.