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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 10 Issue: 04 | Apr 2023

p-ISSN: 2395-0072

www.irjet.net

E-Commerce Product Rating Based on Customer Review Chikkela Sahithi1, Bollu Swapna1, Mulge Harsha1, Sreedhar Bhukya2, Bejjam Vaundhara devi3 1Computer Science & Engineering, Sreenidhi Institute of Science & Technology, Hyderabad, Telangana, India

2Professor,Computer Science & Engineering, Sreenidhi Institute of Science & Technology, Hyderabad, Telangana,

India

3Assistant Professor, Computer Science & Engineering, Sreenidhi Institute of Science & Technology, Hyderabad,

Telangana, India ---------------------------------------------------------------------***--------------------------------------------------------------------form conclusions and judgments. Or, in some cases, there Abstract — Many users purchase products on EC websites.

may be conflicting reviews and ratings. A 5 star product may have verybad feedback. The reason for this may be that some people like to review products, others like to review them. This cannot mandate users to perform both tasks. To make unanimous decisions, I came up with a model that could set things right. It has a very simple but complex working method.

Due to online shopping, many e-commerce companies have been unable to know whether their customers are satisfied with their services. You can track and manage customer reviews by using algorithms to filter out theme and sentiment bias from online customer reviews. The system allows users to see different products and buy products online. Customers submit reviews of products and online shopping services. Specific keywords mentioned in customer reviews are mined and matched against keywords already in the database based on comparison, and thesystem evaluates the products and services offered by the company. This system uses a text mining algorithm to mine keywords. The system reviews various users and based on the reviews the system identifies whether the products and services offered by the e-commerce company are good, bad or worst. The system is a web application that allows users to browse various products online, purchase products, and rate products and online shopping services. This system helps many e-commerce companies improve or maintain their services based on customer ratings, and improve their products based on customer ratings. Key Words: TF-IDF, Sentimental Tokenization, Lemmatization, Stemming.

2. OBJECTIVES The growing popularity of online reviews has also fuelled the fake review writing business. This refers to paid human writers creating misleading reviews to sway reader’s opinions. Our project addresses this problem by building a classifier that takes as input the rating text and basic information from the giver of rating and returns whether the rating is trustworthy or not. This makes it difficult for potential customers to read them and decide whether to purchase the product. Product manufacturers also have problems maintaining overviews and managing customer opinions. Also, many other retailers on his website sell the same product with good reviews, and the manufacturer usually makes many kinds of products, so the manufacturer faces further difficulties.

analysis,

1.INTRODUCTION

3. REVIEW OF RELATED LITERATURE

In today's world, the web has become a great way to expressopinions about products. Your opinion matters a lot, especially when it comes to making decisions about money and time. In these situations, people rely on opinions such as reviews. Like his Facebook, Twitter, etc. on social media, people can discuss their opinions like a product of this research. That's also how we do it. Many people buy products online, but always check reviews and ratings before making a purchase online. This survey helps people save time and have a quick product discussion. Use these people to add sentiment keywords like good, bad, worst, best. Please judge whether this product is good or bad. Sentiment analysis, also known as opinion mining, is a branch of computer research that analyses how people express themselves in written keywords. People can express their opinions through sentiment keywords. It is virtually impossible to read all feedback to

Recently, many classification algorithms have been proposed, but SVM is still one of the most widely and most popular used classifiers. Applying the kernel equations arranges the data instances in such a way within the multidimensional space, that there is a hyper-plane that separates data instances of one kind from those of another. The kernel equations may be any function that transforms the linearly non-separable data in one domain into another domain where the instances become linearly separable. Kernel equations may be linear, quadratic, Gaussian, or anything else that achieves this particular purpose. Once we manage to divide the data into two distinct categories, our aim is to get the best hyper-plane to separate the two types of instances. The data instances that were not linearly separable in the original domain have become linearly separable in the new domain, due to the application of a function (kernel) that transforms the position of the data

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