Sentiment Analysis for Amazon Product Comments

Overview

This project aims to perform sentiment analysis on the comment section of a product listed on Amazon using a variety of machine learning and deep learning methods. The goal is to extract valuable insights from customer feedback and understand the sentiment associated with the product.

Problem Statement

The comment section of an Amazon product can be a treasure trove of valuable information. However, manually analyzing the sentiments expressed in these comments can be time-consuming and impractical. This project seeks to automate this process by leveraging machine learning and deep learning techniques to classify comments into positive, negative, or neutral sentiments.

Methods

Machine Learning Methods

1. Logistic Regression
2. CART (Classification and Regression Trees)
3. XGBoost
4. Naive Bayes
5. Support Vector Machines (SVM)

Deep Learning Methods

1. Convolutional Neural Networks (CNN)
2. Recurrent Neural Networks (RNN)
3. Long Short-Term Memory (LSTM)
4. Artificial Neural Networks (ANN)

Dataset

The project will utilize a dataset of Amazon product comments, which will be preprocessed to remove noise, tokenize the text, and perform feature engineering to extract relevant information for sentiment analysis.

Implementation

The sentiment analysis models will be implemented using Python and popular libraries such as TensorFlow, Keras, Scikit-learn, and XGBoost. The dataset will be preprocessed to remove noise, tokenize the text, and perform feature engineering to extract relevant information for sentiment analysis. The dataset will then be split into training and testing sets, and each machine learning and deep learning method will be trained and evaluated to identify the most effective approach for sentiment analysis. The performance of each method will be rigorously evaluated using a range of metrics including accuracy, precision, recall, and F1 score. Additionally, the models will be compared based on their ability to handle imbalanced classes and their computational efficiency.

This implementation will provide valuable insights into the effectiveness of various machine learning and deep learning methods for sentiment analysis in the context of Amazon product comments.