Fake Product Review Detection and Sentiment Analysis

Online shoppers are becoming increasingly conscious of and sensitive to product reviews. Typically, people use product reviews and ratings before making any purchases from e-commerce websites. Because of this, it is imperative that proprietors of e-commerce websites monitor product reviews and descriptions. Consumers used to hold e-commerce websites accountable rather than the product producers when they sold products with terrible reviews, which might damage the reputation of the e-commerce website brand. Competitors used to occasionally post fake reviews in an effort to increase sales. Therefore, it is crucial for the owner of an e-commerce website to properly do sentiment analysis in order to identify and eliminate fraudulent product reviews from their portal. For the owners of these e-commerce websites, we have developed a product system called "Fake Product."

In order to find-out fake reviews from e-commerce portal, we have developed product review monitoring cum removal system with proper sentimental analysis of genuine reviews framework. This genuine review framework can able to detect fake surveys taken via distinguished IP addresses by social media optimization teams. In this system, we have provided e-commerce owner login id for accessing framework with one secured key. Through that Id, owner can overlook on various items and can give survey about those items. To find out the audit is certified, framework will track IP address of the client if the framework watch counterfeit survey send by a similar IP Address numerous multiple times it will illuminate the administrator to expel that survey from the framework. This system helps the user to find out correct review of the product and remove it from portal if it is fake.

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.