Leveraging ALBERT for Sentiment Classification of Long-Form ChatGPT Reviews on Twitter
The findings highlight the model’s capability to accurately gauge public sentiments toward ChatGPT in the complex landscape of lengthy and nuanced social media content, with implications for understanding public attitudes toward emerging technologies, with potential applications in various domains.
Abstract
: Sentiment analysis of user-generated content on social media sites reveals important information about public attitudes toward emerging technologies. Researchers face challenges in understanding these impressions, ranging from cursory evaluations to in-depth analyses. Analyzing detailed, long-form reviews exacerbates the di ffi culty of 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 such texts. The primary objectives of this research are to improve the accuracy of sentiment analysis for long-form social media texts and to evaluate the e ff ectiveness of the ALBERT transformer-based language model when augmented with data augmentation techniques. 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 e ffi cacy of the ALBERT model, particularly when combined with data augmentation techniques like EDA and Back Translation. The findings highlight the model’s capability to accurately gauge public sentiments toward ChatGPT in the complex landscape of lengthy and nuanced social media content. This advancement has implications for understanding public attitudes toward emerging technologies, with potential applications in various domains.