Abstract
Hit and hot issue about reviews of any product is sentiment classification. Not only manufacturing company of the reviewed product takes decision about its quality, but the customers’ purchase of the product is also based on the reviews. Instead of reading all the reviews one by one, different works have been done to classify them as negative or positive with preprocessing. Suppose from 1000 reviews, there are 300 negative and 700 are positive. As a whole it is positive. Company and customer may not be satisfied with this sentiment orientation. For companies, negative reviews should be separated with respect to different aspects and features, so companies can enhance the features of the product. There is also a lot of work on aspect extraction, and then aspect based sentiment analysis. While on the other hand, users want the most positive reviews and the most negative reviews, then they can decide purchasing a certain product. To consider the issue from users’ perspective, authors suggest a method Multiply-Minus-One (MMO) which can evaluate each review and find scores based on positive, negative, intensifiers and negation words using WordNet Dictionary. Experiments on 4 types of datasets of product reviews show that this method can achieve 86%, 83%, 83% and 85% precision performance.
Highlights
Positive or negative sentence is classified as opinion
Detection of polarity of sentence is known as sentiment analysis
After the extraction of aspect, sentiment analysis can be done on particular features [10][11]
Summary
Positive or negative sentence is classified as opinion. With a single glance, anybody can understand either sentence is positive or negative. Sentiment shifter [18] known as negation In this rule a sentence with positive word followed bynot‘ will get -1 score i.e. not good [-1]. In this polarity of a review can be found but ranking cannot be obtained. The algorithm [18] sums up the sentiment scores of the terms in the review considering negations and intensifiers, here positive score of a word is taken as 1 and negative score as -2. In product reviews it is observed that user express their experiences with product using positive words, negative words, intensifiers and negations. Each examples consist of five steps as mentioned in Table-1
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