In the past few years, micro-blogging platforms, such as twitter, are becoming most popular online social networks. Different opinions and news can be shared about various aspects and occasions using these micro-blogging platforms. Twitter is therefore considered as a rich source of data and it can be used for different text analysis and decision making tasks. The main focus of sentiment analysis is about text classification into positive/negative/neutral feelings based on the polarity of text. The opinions and thoughts on twitter feeds can be expressed in any language. Previous techniques have some limitations in the field of sentiment analysis such as low accuracy, sarcasm, and incorrect classification of tweets. The proposed research focuses on the existing difficulties and complications and presents a framework, for the sentiment detection of twitter feeds, which results in high accuracy and real time performance. There are various pre-processing steps that are applied on twitter feeds to refine them before feeding for sentiment classification. The pre-processing removes slangs and abbreviations with complete words. Three different classification techniques are then used; emoticon analysis, Bag of words and SentiWordNet. The experimental evaluation confirms that the proposed algorithm dynamically increases the precision, recall, f-measure and most importantly accuracy when compared with other similar techniques.