In this research, a sentiment analysis approach is proposed to extract sentiments associated with positive or negative polarities in news articles. Information retrieval agents were implemented to capture important keywords from the unstructured data such as news, distinguished the keywords into positive or negative lexicons, rank those keywords based on the frequency of occurrences, and recommend the news articles as positive or negative to the users. The proposed approach is illustrated with experimental results and their main implications are discussed.