Leveraging ALBERT for Sentiment Classification of Long- Form ChatGPT Reviews on Twitter

Abstract

Sentiment analysis of content created by users on social media sites reveals important information on public attitudes toward upcoming technologies. Researchers have challenges understanding these impressions, ranging from cursory evaluations to in-depth analyses. Focusing on detailed, long-form reviews exacerbates the difficulty in achieving accurate sentiment analysis. This research addresses the challenge of accurately analyzing sentiments in lengthy and unstructured social media texts, specifically focusing on ChatGPT reviews on Twitter. The study introduces advanced natural language processing (NLP) methodologies, including Fine- Tuning, Easy Data Augmentation (EDA), and Back Translation, to enhance the accuracy of sentiment analysis in lengthy and unstructured social media texts. The primary objective is to evaluate the effectiveness of the ALBERT transformer-based language model, in sentiment classification. Results demonstrate that ALBERT, when augmented with EDA and Back Translation, achieves significant performance improvements, with 81% and 80.1% accuracy, respectively. This research contributes to sentiment analysis by showcasing the efficacy of the ALBERT model, especially when combined with data augmentation techniques like EDA and Back Translation. The findings highlight the model's capability to accurately gauge public sentiments towards ChatGPT in the complex landscape of lengthy and nuanced social media content. This advancement has implications for understanding public attitudes towards emerging technologies, with potential applications in various domains.