Twitter Data Preprocessing for Sentimental Analysis

Abstract

With the innovation of web and its development, there is a large amount of information present in the web for web clients and a great deal of information is produced daily as well. Informal communication destinations like Twitter, Facebook, Google+ are quickly picking up prominence as they enable individuals to share and express their views, post messages over the world. Lot of work has been done to find the Sentiment of the Tweets. Identifying the sentiments behind the tweets helps in Business, where marketing companies develop strategies by recording the nature as well as quantify the respond by the customer during new product launch. Also useful in politics, where people view can be tracked, understanding the consistency of the statements at the government level, even used to predict the results of the election. In this paper, we give a study and a near investigations of existing methods for assessment mining like AI and vocabulary-based methodologies, together with assessment measurements. Utilizing different machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we give polarity on twitter information.