Echoes of Emotion : Sentiment Analysis of Customer Reviews

Echoes of Emotion is a comprehensive project aimed at analyzing the sentiment of Amazon food reviews using various techniques, including VADER and a fine-tuned pretrained RoBERTa model. This project processes a large dataset of customer reviews, applies text preprocessing techniques, and compares the performance of different models to classify reviews into three sentiment categories: positive, negative, and neutral.

Dataset

Visit here to download the dataset: AFFR Kaggle

Features

Data Processing

• Dataset: Processes a large dataset of Amazon food reviews.
• Text Preprocessing: Applies techniques such as word tokenization, part of speech tagging, and named entity recognition.

Sentiment Analysis Techniques

• VADER: Uses VADER for rule-based sentiment analysis.
• RoBERTa: Implements a fine-tuned pretrained RoBERTa model for context-aware sentiment scoring.

Model Comparison

• Performance Comparison: Compares the performance of VADER and RoBERTa models in classifying sentiments.

Implementation

• Programming Language: Python
• Libraries Used: NLTK, Scikit-learn, Hugging Face's Transformers

Installation

Follow these steps to set up the project on your local machine:

1. Clone the repository:

   git clone https://github.com/krishnaura45/Echoes-of-Emotion.git
   cd Echoes-of-Emotion

2. Install required dependencies:

   pip install -r requirements.txt

3. Download the dataset:

   • Ensure you have downloaded the Amazon food reviews dataset from kaggle and placed it in the appropriate directory.