Enhancing Sentiment Analysis through Text Classification Using Data Mining Approach

This study offers a thorough examination of sentiment analysis methods, including both deep learning architectures and conventional machine learning algorithms. The advantages and disadvantages of each strategy are investigated via methodical testing and analysis, providing insightful information for natural language processing practitioners and researchers. To maximise the effectiveness of the sentiment analysis model, several factors are investigated, including feature representations, model hyperparameters, and dataset properties. Conventional approaches such as Naive Bayes and Support Vector Machines provide robustness and simplicity, whereas deep learning techniques like Convolutional and Recurrent Neural Networks are excellent at identifying intricate patterns in textual data. Comprehending the balance between interpretability, performance, and scalability of a model facilitates well-informed decision-making for particular application needs. All things considered, this study advances sentiment analysis techniques and makes it easier for them to be used successfully in practical settings.