Transforming Sentimental Analysis: Applying Deep Learning with BERT and GPT for X Data Insights

Social media platforms like X have become key channels for expressing opinions and sentiments, creating vast amounts of textual data. Analyzing this data to understand public sentiment is crucial for various applications, including market research, political analysis, and customer feedback. Traditional sentiment analysis methods often struggle with the nuances of language, especially in informal settings like X. However, recent advancements in deep learning, particularly with transformer models such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), have significantly improved the accuracy of sentiment analysis. These models use attention mechanisms to capture the context of words within a sentence, allowing for a more sophisticated understanding of sentiment. Unlike existing studies that predominantly focus on either BERT or GPT separately, this research provides a comparative evaluation of their effectiveness for sentiment analysis on X, highlighting their strengths and limitations in handling informal and context-dependent text. BERT, with its bidirectional approach, and GPT, known for its text generation capabilities, have achieved state-of-the-art results in numerous NLP tasks. This study leverages these models to analyze the Sentiment140 dataset, aiming to enhance sentiment analysis on X by utilizing deep learning techniques and libraries like Hugging Face Transformers, PyTorch, and TensorFlow.