APPLICATION OF MACHINE LEARNING TECHNIQUES IN SENTIMENT ANALYSIS

Abstract

There is ample amount of statements on social sites which can be inferred with the assistance of sentiment examination. This is very beneficial to find the public views. Sentiment Analysis includes catching of client's conduct, likes and dislikes from the created web content. There is no solid meaning of "Sentiments", yet by and large they are considered as musings, perspectives and frame of mind of an individual emerging basically dependent on the feeling rather than an explanation. Many of clients utilize social destinations to express their sentiment about brands, services, beliefs or opinions about things, political and religious views, emotions, personalities or places and people they interact with. This data is mostly unorganized, slangs, etc. and therefore, text analytics and natural language processing are utilized to separate and group this data. Any Non contextual and irrelevant contents are identified and discarded. The classification of sentiments will be performed on this data, which goes as follows: a training data set is created manually and based on this training data set sentiment analysis is performed on the twitter comments. Machine learning, for example, a hybrid Naive Bayesian classifiers are utilized for sentiment categorization with lexical word reference and natural language processing.