Python
Aim:
To detect SKL-Based fake reviews on e-Commerce with using couples of machine learning algorithm based on sentiment analysis.
Abstract:
The outbreak of Covid-19 and the enforcement of lockdown, social distancing, and other precautionary measures lead to a global increase in online shopping. The increasing significance of online shopping and extensive use of e-commerce has increased competition between companies for online selling. Highlights that online reviews play a role in boosting a business or slandering it. Product review is an essential factor in customers’ decision-making, leading to an intense topic known as fraudulent or fake reviews detection. Given these reviews’ power over a business, the treacherous acts of giving false reviews for personal gains have increased with time. In our research, we proposed a fake review detection model by using Text Classification and techniques related to Machine Learning. We used classifiers such as Support Vector Machine, K-Nearest Neighbor, and logistic regression (SKL), using a bigram model that detects fraudulent reviews based on the number of pronouns, verbs, and sentiments.
Synopsis:
Fake review detection on e-commerce based on sentimental analyzing in machine learning to predict. We proposed a methodology using machine learning-based text classification that helped determine whether the given comments on a particular product service are real or fake.
Existing System:
Semi-supervised classifiers are used to detect online spam reviews using a dataset of hotel reviews. To fulfill our objective, we observed a dataset on hotel reviews and applied machine learning techniques and text classification methods to detect reviews that are not genuinely made.
Problem Definition:
Supervised learning is conventionally used to detect fake reviews, but it also has some restrictions, such as assurance of the quality of reviews in the training dataset. We proposed a Machine learning algorithm based for fake reviews detection in the e-commerce industry.
Proposed System:
We proposed a first thing preprocess a data set then implement into algorithm support vector machine, K-Nearest Neighbor and Linear Regression (SKL) based algorithm for fake reviews detection in the e-commerce industry. To fulfill our objective, we observed a dataset on amazon reviews and applied machine learning techniques.
Advantage:
Semantic analysis of Twitter messages, movie reviews, and spam detection from SMS and email data sets. The results showed that the proposed methods smoothed the learning process and gave better results in the experiments.
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