Description
Online shoppers are becoming increasingly aware of the prevalence of fake product reviews. Before making a purchase from an online retailer, consumers often rely on product reviews and ratings. Therefore, it is crucial for e-commerce website owners to monitor product descriptions and reviews closely. In the past, when e-commerce websites featured products with negative reviews, consumers would blame the website rather than the product manufacturers, potentially damaging the brand’s reputation. Competitors would sometimes post fake reviews to boost sales. To combat this, e-commerce website owners must effectively conduct sentiment analysis to detect and remove fake product reviews. Our system, “Fake Product,” is designed to assist online store owners in this process.
We have developed a product review monitoring and removal system that utilizes sentiment analysis to distinguish between genuine and fake reviews on e-commerce portals. This system can identify fake reviews generated by social media optimization teams using unique IP addresses. E-commerce owners are provided with a secure login ID to access the system, allowing them to view and provide feedback on various products. The system tracks the IP addresses of users submitting reviews to verify their authenticity. If the system detects repeated submission of fake reviews from the same IP address, it alerts the administrator to remove the reviews. This technology helps customers find reliable product reviews while eliminating fake ones from the portal.
The process of identifying and removing fake product reviews involves several steps:
1. Data Preprocessing: Formatting and cleaning up e-commerce portal product review data to prepare it for machine learning algorithms.
2. Tokenization: Breaking down the data into words and phrases to analyze the content of reviews.
3. Stop-word Elimination: Removing irrelevant words from reviews to focus on meaningful content.
4. Bag-of-words Model: Processing data for natural language processing by assigning subjectivity scores to individual words.
5. Training the classifier: Training the system to identify fake product reviews using predictive analysis.
6. Sentiment Analysis: Utilizing Decision Tree Classifier and Naive Bayes algorithms to analyze and compare results for fake product reviews.
By implementing proper sentiment analysis techniques, we can effectively identify and remove fake product reviews from our portal. Additionally, the project “Fake Product Review Detection and Sentiment Analysis” includes static pages such as Home, About Us, and Contact Us, as well as technologies like HTML, CSS, JavaScript, Python, MySQL, and Django. The project can be configured on various operating systems including Windows, Linux, and Mac.
