SENTIMENT ANALYSIS USING MACHINE LEARNING

Sentiment analysis, or opinion mining, has become a vital tool in understanding public perception across various domains such as product reviews, social media, and news content. However, traditional sentiment analysis models often suffer from performance degradation when applied to data from a different domain than they were trained on, due to domain-specific vocabulary and contextual differences. This research focuses on cross-domain sentiment analysis using machine learning techniques to address the challenge of domain adaptation. We explore various feature extraction methods, such as TF-IDF and word embeddings, and implement multiple machine learning algorithms including Support Vector Machines , Naïve Bayes, and Random Forest to evaluate their effectiveness in cross-domain settings. Furthermore, domain adaptation techniques such as instance weighting and feature alignment are employed to improve model generalization. Experimental results on benchmark datasets demonstrate that incorporating domain adaptation significantly enhances the model’s ability to correctly classify sentiments in unseen domains. This work contributes to the development of robust and scalable sentiment analysis systems capable of operating effectively across diverse data sources.