Integrating BERT for Nuanced Sentiment Analysis: A Detailed Examination of Diverse Textual Datasets - Esra Issa; Abdullahi Abdu Ibrahim (IEEE Access)
The rapid emergence and growth in the number of digital communication platforms have resulted in a previously unimaginable volume of text data suitable for sentiment analysis. Public sentiment extraction and interpretation definitive in various areas of marketing, politics, and customer service sectors. Existing approaches prove inadequate to address language complexity and subtlety. Consequently, more advanced analytical tools are required. Therefore, this study employs a BERT model in performing sentiment analysis on textual data associated with ChatGPT, an AI conversational tool OpenAI develops. The datasets utilized in this research are three, which are Data Collection, ChatGPT Sentiment Analysis Dataset, and ChatGPT App Reviews Dataset. They range from topics such as tweets to comprehensive reviews and thus provide diverse backgrounds for testing the model’s applicability. The BERT model is chosen for its strong processing abilities, especially due to its capacity to understand text nuances. It applies sentiment analysis in six training epochs, and the results indicated a model competent in accurate sentiment classification, irrespective of language and sentiment uniqueness. The overall performance was ousttanding, as evidenced by the model’s accuracy and its classification precision in both datasets. These findings have the potential to make the BERT model dominant in all sentiment analysis tasks. The analysis would prove instrumental in providing in-depth public opinion and sentiments made possible by the model’s credible results. In this stutdy, it is clear that BERT models can significantly improve machine learning towards better understanding and interacting in human language. These results also provide a basis for further research into these modalities across real-time applications or further modification that would develop the model to be exposed to different kinds of data.