Published Paper Details:

The E-commerce websites have been emerged in a high range of marketing benefits for the users to publish or share the experience of the received product by posting review that contain useful comments, opinions and feedback on the product. Nowadays, millions of customers gain opportunities to compare similar products in online websites and pick their favorites in digital retailers, such as Amazon.com and Taobao.com. Customer reviews in social media and electronic commerce Websites contain valuable electronic word information of products. Sentiment Analysis is widely applied as voice of customers for applications that target marketing and customer service. Sentiment extractors in their most basic form categorize texts as either having a positive or negative or sometimes neutral sentiment. A common application of sentiment analysis is the automatic determination of whether an online review contains a positive or negative assessment. Hence, in this paper, with the help of the techniques on sentiment analysis, opinionated sentences referring to a specific feature are first identified from product online reviews. We have proposed deep learning method as a classification model for finding the state of review. The results showed recommended site for the customer based on the early reviews, past reviews and answer given to query review for the customer. Moreover, it is observed that the proposed technique can able to reply all the reviews with a better similarity like a human response to the customer.