Journal of Intelligent Computing Munazza Ishtiaq 6 1 2015 Sentiment analysis has attained much attention in the recent years due to its significance in various fields as it captures and analyzes such attitudes and opinions in an automated and structured fashion and offers a powerful technology to a number of problem domains. This research is based on the use of a novel unsupervised approach for sentiment analysis of twitter data using a rule based scoring engine. The focus of this approach is on POS tagging in which the parts-of-speech are ranked according to their sentiment describing influence. A novel term is devised for POS which is named as “sentiment influencers” and are ranked according to their influence on detecting sentiment. Results have shown that appropriate ranking of POS provides good results than its normal usage.