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.
