Sentiment analysis aims to identify the orientation of opinions in text, whether positive, negative, or neutral. It draws from fields like cognitive science, natural language processing, and machine learning. Challenges include domain dependence, sarcasm, and contrasting with standard text categorization where word presence indicates category. Approaches include subjectivity detection using graph algorithms, sentiment lexicons capturing word sentiment, and scoring adjective-adverb combinations. Applications include review analysis, question answering, and developing "hate mail" filters. Future work includes exploring the cognitive perspective on sentiment analysis.