Sentiment analysis or opinion mining is an imperative research area of natural language processing. It is used to determine the writer’s attitude or speaker’s opinion towards a particular person, product or topic. Polarity or subjectivity classification is the process of categorizing a piece of text into positive or negative classes. In recent years, various supervised and unsupervised methods have been presented to accomplish sentiment polarity detection. SentiWordNet (SWN) has been extensively used as a lexical resource for opinion mining. This research incorporates SWN as the labeled training corpus where the sentiment scores are extracted based on the part of speech information. A vocabulary SWN-V with revised sentiment scores, generated from SWN, is then used for Support Vector Machines model learning and classification process. Based on this vocabulary, a framework named “Enhanced Sentiment Analysis and Polarity Classification (eSAP)” is proposed. Training, testing and evaluation of the proposed eSAP are conducted on seven benchmark datasets from various domains. 10-fold cross validated accuracy, precision, recall, and f-measure results averaged over seven datasets for the proposed framework are 80.82%, 80.83%, 80.94% and 80.81% respectively. A notable performance improvement of 13.4% in accuracy, 14.2% in precision, 6.9% in recall and 11.1% in f-measure is observed on average by evaluating the proposed eSAP against the baseline SWN classifier. State of the art performance comparison is conducted which also verifies the superiority of the proposed eSAP framework.