Analysis and Implementation of Sentiment Classification Using Lexical POS Markers

Natural language processing has attracted many researchers as the amount of information available on the internet and other media is increasing exponentially. So we need computer interference to process that enormous data.NLP does exactly the same by converting the natural language into something to be understood by computer to process. Though it has a lot of branches I am working in sentiment analysis of text. By this we mean a method to find what the reader is thinking when he/she was writing a particular text. This helps in tracking possible suicidal case, terrorist attacks and also in finding the orientation of a particular product and changing ourselves according to customer reviews. Polarity analysis is a subset of sentiment analysis when we find the polarity or orientation of a particular word and thus finding the overall polarity of a sentence, this is useful in finding whether a particular review of a product/movie/hotel or anything else is good or not [1]. With the ever increasing number of websites available free to post reviews today’s customer are smart to follow the reviews before purchasing a product. While many review sites, such as Epinions, CNet and Amazon, help reviewers quantify the positivity of their comments, sentiment classification can still play an important role in classifying documents that do not have explicit ratings.