Open AccessDesign of NLP technique fore-customer review
Author(s) -
Anjali Dadhich,
Blessy Thankachan
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c4614.099320
Subject(s) - computer science , purchasing , naive bayes classifier , product (mathematics) , quality (philosophy) , ask price , task (project management) , classifier (uml) , world wide web , data science , artificial intelligence , marketing , business , engineering , support vector machine , philosophy , geometry , mathematics , systems engineering , epistemology , finance
With the passage of time and the growth of ecommercea new web world needs to be built their users can share their ideas and opinions differently domains.There are thousands of websites that sell these various products. The quick growth in the number of reviews and their availability and the arrival of rich reviews for rich products for sale online, the right choice for many products has been difficult for users. Consumers will soon be able to verify the authenticity and quality of the products. What better way is there to ask people who have already bought the product? That’s where customer reviews come from. What’s worse is the popular products with thousands of updates — we don’t have the time or the patience to read all of them thousands. Therefore, our app simplifies this task by analysing and summarizing all the reviews that will help the user determine what other consumers have experienced in purchasing this product. This function focuses on mining updates from websites like Amazon, allowing the user to write freely to view. Automatically removes updates from websites. It also uses algorithms such as the Naïve Bayes classifier, Logistic Regression and SentiWordNet algorithm to classify reviews as good and bad reviews. Finally, we used quality metric parameters to measure the performance of each algo.