machine learning projects for final year – MACHINE LEARNING ANALYTICS WITH TWITTER DATA ON SENTIMENT ANALYSIS
With the improvement of web technology and its evolution, there is a huge volume of data current in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging feelings and sharing opinions. Feature extractors and different Machine Learning Classifiers are used here. The unigrams and feature extractors are unigrams, with weighted decisive and pesimisive keywords. A framework is planned that separates feature extractors and classifiers as a pair of components. It has been practical that people currently tend to look upon evaluations of products which are available online before they buy them. And for several businesses, the online belief decides the success or failure of their product. Thus, Sentiment Analysis plays an significant role in businesses. Businesses also wish to extract sentiment from the connected reviews in order to progress their products and in turn their status and help in customer satisfaction. Semantics: The comprehensive sentiment of a tweet is classified by the algorithms. Semantic role labeler can be used which shows which noun is allied with the verb and therefore the classification occurs.