Twitter has become one of the most popular micro blogging platforms recently. Near about 800 Millions of users can uses twitter micro-blogging platform to share their thoughts and opinions about different aspects? Therefore, Twitter is considered as a rich source of huge amount of information for decision making, data mining and Sentiment analysis. Sentiment analysis refers to a classification problem where the main focus is to predict the polarity of words and then classify them into positive, negative and neutral feelings with the aim of identifying attitude and opinions that are expressed in any form or language. Sentiment analysis over Twitter offers organizations a fast and effective way to monitor the public’s feelings towards their products, brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. The primary issues in previous techniques are data sacristy, classification accuracy, and sarcasm, as they incorrectly classify most of the tweets with a very high percentage of tweets incorrectly classified as neutral. This work focuses on these problems and presents a supervised learning algorithm for twitter feeds classification based on a hybrid approach. The proposed method includes various pre-processing steps before feeding the text to the classifier. Experimental results show that the proposed technique overcomes the previous limitations and achieves higher accuracy, precision and higher recall when compared to similar techniques.

Opinion Mining Sentiment Analysis hybrid supervised learning Methods Social Media.