Abstract
Emergence towards valuing customer reviews and their opinions is the prime propelling factor for any exploring business. Electronic commerce has clinched the world, and the majority preferring to buy products through these websites online. Due to the increase in demand for e-commerce with customer’s preference towards online purchasing of products over physically moving from shop to shop (offline purchasing), there is the huge amount of information being shared to and fro. The e-commerce websites are loaded with immense volume of data and customer reviews thus being generated. This huge volume of data is in its diversity and its structural randomness. The customers face difficulty in precisely finding the review for a particular feature of a product that they intend to buy. Also, there are mixtures of positive and negative reviews thereby increasing the complexity for customers to find a cogent response. So to avoid this confusion and make this review base more transparent and user friendly, a technique to extract feature-based opinion from a diverse pool of reviews and processing it further to segregate it with respect to the aspects of the product and further classifying it into positive and negative reviews using machine learning-based approach. The analysis of the data generated in huge amount holds the prime centred topic, underlying data analytics. This paper proposes the study and analysis of obtaining the best methodologies on sentiment analysis of consumer reviews in context to the features of a product. The system aims at providing a summary that represents the extent to which the consumers who had already bought the particular product were or were not satisfied with the specific feature of the product. Due to this sentiment analysis, there is a feedback environment being generated for helping customers buy the right product and guiding companies to enhance the features of product suiting consumer’s demand.
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