AN EFFICIENT DOMAIN INDEPENDENT APPROACH FOR SENTENCE LEVEL SENTIMENT ANALYSIS.

Sentiment Analysis: The process of computerized perception of subjectivity (i. e, positive, negative and neutral) of the sentences is called as sentiment analysis. Now a days the way how the information is being conceived or represented (in the form of reviews) by the people is playing a vital role in the improvement and growth of any person or organization (such as companies). Polarity finding or subjectivity quarrying of the sentences which are communicated in different zones is the central theme of sentiment analysis. After performing a lot of research on sentiment analysis, we have concluded that there exist various challenges on sentiment analysis, such as comparative sentences, sarcastic sentences, semantic ambiguous statements and domain specific adaptive statements etc. which were unable to be resolved properly so far. So we strictly decided to find an algorithm to resolve any one of the above challenges. Our discovered algorithm is unsupervised learning algorithm which will make use of collected and well defined sentences, Noun-Verb associated files, senti word net and Stanford parser. The basic criteria of our algorithm deals with the collection of data as sentences (By using Stanford parser) from online review sites of various domains (such as online shopping, mobile etc..), POS tagging for collected sentences, Feature extraction and selection from collected sentences and finally deciding Polarity. Apart from algorithm, the below report also includes research work on existed methods and comparisons between existed methods and our proposed method. The major achievement of our algorithm is increased accuracy compared to other existing methods.