Paper Title
SENTIMENT ANALYSIS OF SOCIAL MEDIA POSTS: UNCOVERING PUBLIC OPINION THROUGH COMMENTS

Abstract
Abstract - This research paper investigates the application of sequential models, specifically Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs), to enhance the accuracy and efficiency of sentiment analysis in textual data. By leveraging advancements in deep learning and sequential modeling techniques, the study aims to capture contextual dependencies and temporal patterns present in text, crucial for understanding human emotions. The proposed methodology involves preprocessing the data, sentence tokenization, and training on large datasets to enable sequential models to recognize sentiment patterns and generalize effectively. Experiments conducted on benchmark sentiment analysis datasets compare the performance of the sequential model-based approach with traditional methods, highlighting its effectiveness. The impact of varying model architectures, hyperparameters, and training strategies on sentiment analysis accuracy is explored. Real-world applications are demonstrated in domains such as social media sentiment tracking and product review analysis, showcasing the adaptability of the sequential model. Hyperparameter tuning is performed to increase sentiment analysis accuracy, and real-time data extraction via web scraping or API ensures a continuous stream of information for analysis. The paper concludes by discussing challenges and suggesting avenues for future research in sentiment analysis using sequential models. Findings underscore the potential of sequential models to improve sentiment analysis accuracy and robustness, emphasizing the importance of hyperparameter tuning and real-time data extraction for enhancing model capabilities. Keywords - Sentiment Analysis, Sequential Model, Comment Extraction, Machine Learning.