Advancing Sentiment Analysis Precision through ConvNet-SVMBoVW Mode in a Hybrid Context-Enhanced Deep Learning Paradigm

Sentiment analysis stands as a crucial component of natural language processing (NLP), tasked with interpreting sentiments expressed within text and categorizing them as positive, negative, or neutral. This research study introduces a novel methodology, the ConvNet-SVMBoVW Mode, within a hybrid context-enhanced deep learning paradigm, aimed at enhancing sentiment analysis precision. By integrating advanced deep learning techniques with context-enhancement strategies, the study seeks to improve accuracy and robustness in sentiment analysis. The research explores various text and image preprocessing techniques, ensuring comprehensive data preparation crucial for effective sentiment analysis. The proposed model achieves sentiment classification accuracy ranging from 85% to 92%, addressing the existing challenges in sentiment analysis tasks and advancing state-of-the-art methodologies in natural language processing applications.