ML based Opinion mining online Customer Reviews


ML based Opinion mining online Customer Reviews


As online marketplaces have been popular during the past decades, online sellers and merchants ask their purchasers to share their opinions about the products they have bought. As a result, millions of reviews are being generated daily which makes it difficult for a potential consumer to make a good decision on whether to buy the product. ML based Opinion mining online Customer Reviews


  • The project uses opinion mining to analyze customer reviews about mobile phones sold on
  • The objective is to develop a prediction system that can be used to classify an input customer review of a mobile phone as a good review or a bad one.


As the commercial side of the world is almost fully undergone in online platform people is trading products through the different eCommerce website. And for that reason reviewing products before buying is also a common scenario. Also now a day, customers are more inclined towards the reviews to buy a product. So analyzing the data from those customer reviews to make the data more dynamic is an essential field nowadays. In this age of increasing machine learning-based algorithms reading thousands of reviews to understand a product is rather time-consuming where we can polarize a review on a particular category to understand its popularity among buyers all over the world.


Sentiment classification aims to determine the overall intention of a written text which can be of admiration or criticism type. This can be achieved by using machine learning algorithms. So, the problem that is going to be investigated in the project is as follow:

Which machine learning approach performs better in terms of accuracy on the Amazon beauty products reviews?

Proposed System

In our proposed approach we have used several Machine learning algorithms like support vector machine (SVM), Random Forest (RF), Logistic Regression (LR), and applied.

  1. First, we take the Amazon product review dataset.?
  2. Filter dataset according to requirements and create a new dataset which has attributes according to analysis to be done
  3. Perform Pre-Processing on the dataset
  4. Split the data into training and testing
  5. Train the model with training data then analyze testing dataset over classification algorithm
  6. Finally, you will get results as accuracy metrics


Product review



  1. Windows 7,8,10 64 bit
  2. RAM 4GB


  1. Python 2.7 or 3.x / Anaconda Navigator


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