Investigation of feature based cluster techniques for product ranking through customer reviews

Abstract

newlineNowadays Sentiment Analysis SA systems are very popular since most of the people trust it for decision making purpose about the product service news analytics etc based on the opinions emotion attitudes and feelings expressed by the users through reviews Sentiment analysis is used to detect the sentiment present in the reviews through positive negative or neutral opinion about the features of the products or services automatically by relying on certain algorithms A study says that nearly 89 of the customers trust the online reviews posted by the reviewer user writes the review after using the products for their decision making process during purchase Sentiment Analysis systems will be used by the customer for buying new products among the alternatives However it is also used by the manufacturer to understand the strength and weakness of their products Manufacturer can also use the SA system to improve their weakness specified by the consumers through reviews At present the sad aspect is that many spammers post the irrelevant or fake reviews about certain products to increase or decrease its market share among others Sentiment Analysis system face great difficulties in deploying the algorithms to classify each review as either honest review posted by the genuine customers after using the products or spam review posted by the individual spammer or spammer groups Another major challenge faced by the Sentiment Analysis system is that it lacks accuracy of predicting the both Explicit and Implicit features of the review sentences in dataset Lastly ranking among the