Multi label text classification based on Amazon Product Review, Find the topic/area which the review belogs to
Identify key aspects of a Amazon Product Review
E-commerce has revolutionized retail experience through multiple insights that it provides customers and retailers. Customer' reviews are considered as feedback by retailers about their product and services. These feedback allow them to understand the industry better and enhance the efficiency and quality of their product.
A thorough analysis of a review can provide insights to retailers and E-commerce platforms about improvements that are required for better customer satisfaction. Analysis of such million reviews are handled by the E-commerce team manually and it is a very time-consuming task. This impacts the business of retailers as they get delayed feedback.
here i created a simplified Machine Learning approach to analyze the feedback & identify the key problems that are highlighted.
Dataset train.csv and test.csv
I solved this problem using Machine Learning with TfidfVectorizer and DecisionTreeClassifier I created a Python Jupyter Notebook which contain the complete project includes data preprocessing and ML Model Training.
Software Requirement
Pytorch, numpy, pandas, torchvision, sklearn, scipy, matplotlib, seaborn
Installation.
You can install all the softwares saperately or just run this command with requirements.txt given in the zip
pip install -r requirements.txt
Finally create a folder name "save" in the same place where put the notepad file for saving the model temporarily