Fake Product Review Detection and Sentiment Analysis

Online buyers are become more aware of and perceptive of fake product reviews. Most of the time, consumers consult product reviews and ratings before making any purchases from online retailers. Consequently, it is essential for owners of e-commerce websites to keep an eye on product descriptions and reviews. When e-commerce websites offered products with awful reviews, consumers used to hold them liable instead of the product manufacturers, which might harm the reputation of the e-commerce website brand. Former rivals would periodically publish fake reviews in an attempt to boost revenue. Therefore, in order to detect and remove phony product reviews from their portal, an e-commerce website owner must do sentiment analysis correctly. We have created a system of products for the proprietors of these online stores that we name "Fake Product."

We have created a product review monitoring and removal system with an appropriate framework for the sentimental analysis of real reviews in order to identify false reviews from e-commerce portals. This authentic review architecture is capable of identifying phony surveys that social media optimization teams have taken using unique IP addresses. We have given the e-commerce owner a login ID in this system so they may access the framework using a single, secure key. The owner can see and provide feedback on a variety of products using that ID. The framework will follow the client's IP address to determine whether the audit is certified. Should the framework detect the repeated sending of a fraudulent survey by the same IP address, it will alert the administrator to remove the survey from the system. This technology assists the customer in locating accurate product reviews and removes phony ones from the portal.

Following section represents Sentimental Analysis process to identify products fake reviews and how to remove them from portal.

Step 1: Data Preprocessing: Here, we have processed e-commerce portal product review data using following steps:

• First of all we start formatting data to represent it in a proper format for ML
• In second step, we clean up data to remove incomplete variables
• At last, we sample data and reduce it in run time for algorithms and memory requirements.

Step 2: Tokenization: In this step, we usually break the data into words, phrases and meaningful elements in order to explore the words presents in a sentence.

Step 3: Stop-word Elimination: Here , we mainly focused on text mining to identify negative stop words presents in reviews which should not be part of opinion and remove them from reviews.

Step 4: Bag-of-words Model: Here, we have to process our data for NLP and we only take here individual words into account to allot them specific subjectivity score.

Step 5: Training the classifier: Here, in this section we train our system for identification of fake product reviews by using predictive based test data analysis.

Step 6: Sentimental Analysis: For doing sentimental analysis of fake product reviews from database, here we take the use of Decision Tree Classifier and Naive Bayes and comparing the results.

In this article, we illustrated how with the help of proper sentimental analysis, we can identify fake product reviews and can able to remove it from our portal.

Static Pages and other sections :

These static pages will be available in project Fake Product Review Detection and Sentiment Analysis

• Home Page with good UI
• Home Page will contain an animated slider for images banner
• About us page will be available which will describe about the project
• Contact us page will be available in the project

Technology Used in the project Fake Product Review Detection and Sentiment Analysis

We have developed this project using the below technology

• HTML : Page layout has been designed in HTML
• CSS : CSS has been used for all the desigining part
• JavaScript : All the validation task and animations has been developed by JavaScript
• Python : All the business logic has been implemented in Python
• MySQL : MySQL database has been used as database for the project
• Django : Project has been developed over the Django Framework

Supported Operating System

We can configure this project on following operating system.

• Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install Python, PIP, Django.
• Linux : We can run this project also on all versions of Linux operating system
• Mac : We can also easily configured this project on Mac operating system.