Publication

Abstract : People often find themselves in a situation where they need to make simple decisions, for example, what book to read, or which movie to watch, or which smart phone to buy. In these kinds of situations, one often seeks the opinion of others in the form of IMDB (International Movie Database) rating or magazine reviews or online product reviews etc. The general opinion about a product or a publication or a service or a company is usually expressed in the form of customer reviews and the web has facilitated sharing and expressing opinions in the form of free style texts which is unstructured or semi structured. In this paper, we introduce a sentence level, sentiment polarity calculation that identifies complex sentential structures and modifies the sentence polarity accordingly. The sentiment polarity of a text is given by a score that lies in the range [-1,1] and denotes the positivity of the tone of the text’s author. Thus, the sentiment polarity or the sentiment score of a review can be used to perceive the opinion of the reviewer. The average polarity of all the words in a sentence gives the polarity of a sentence. The polarity of all the common words in the English dictionary is retrieved from CLiPS Pattern module (BSD license) for Python. After obtaining the polarity of a sentence, it is modified based on the structure of the sentence which is identified using a set of heuristic rules that use POS (Parts of Speech) tagging to identifyrelationships between words. Then the average polarity of all the sentences in a review, gives the overall polarity of the review. We have compared the results against the inbuilt function pattern.en.sentiment (sentence) and based on empirical evidence the result are more accurate in many cases and in other cases the results are the same.