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Abstract
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The rapid expansion of social media and online platforms has resulted in a vast amount of textual data, offering valuable insights into public sentiment, opinions, and behaviors. Sentiment analysis, a key application of Natural Language Processing (NLP), seeks to classify sentiments expressed in text as positive, negative, or neutral. Traditional approaches often struggle with language complexities and contextual nuances, necessitating more robust solutions. Deep learning models, particularly Convolutional Neural Networks (CNNs), have demonstrated effectiveness in capturing hierarchical features for sentiment analysis. This research investigates the integration of CNNs with Karush-Kuhn-Tucker (KKT) conditions—a mathematical optimization framework—to enhance sentiment classification accuracy and computational efficiency. By addressing challenges such as sarcasm detection, generalization across diverse datasets, and real-time processing demands, the study explores novel applications of KKT conditions within deep learning frameworks. The findings aim to advance NLP methodologies by offering scalable and accurate sentiment analysis models applicable to domains like customer feedback analysis, social media monitoring, and market research.
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