1) Create Amazon Product Review Sentiment Analysis
NOTE:
Download the Glove Embedding from Kaggle here : https://www.kaggle.com/datasets/danielwillgeorge/glove6b100dtxt
Project Overview
This repository contains a machine learning project focused on performing sentiment analysis on Amazon product reviews. The aim is to classify the sentiments expressed in the reviews as either positive or negative. This can assist businesses in automating the analysis of customer feedback and enhance user experience through improved product recommendations.
Problem Setup
The task is framed as a binary classification problem:
- Input: Text of a product review.
- Output: Sentiment classification (Positive or Negative).
Data Used
The dataset comprises reviews collected from Amazon, each labeled as either positive or negative based on the sentiment expressed by the reviewer. The dataset includes:
- Review Text: The text content of the review.
- Sentiment Label: Binary labels (Positive or Negative).
Sample Data Structure
{
"reviewText": "Great product, loved it!",
"sentiment": "Positive"
}
Technologies and Techniques Used
- Python: For all backend operations including data handling and model training.
- TensorFlow/Keras: To build and train the neural network.
- Natural Language Processing: Techniques such as tokenization and vectorization to convert text data into a form that can be fed into the model.
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