Echoes Of Emotions : Sentiment-analysis-project

This is a project description for "Echoes of Emotion", a sentiment analysis project that focuses on Amazon food reviews. Here's a breakdown of the key points:

Project Goal: Analyze the sentiment of Amazon food reviews using various techniques.

Techniques Used:

1. VADER (rule-based sentiment analysis)

Key Features:

1. Process large dataset of Amazon food reviews
2. Apply text preprocessing techniques:
   • Word tokenization
   • Part of speech tagging
   • Named entity recognition
3. Compare performance of VADER in sentiment classification

Implementation Details:

1. Programming language: Python
2. Libraries used:
   • NLTK
   • Scikit-learn

Setup Instructions:

1. Create a virtual environment and activate it:

   python -m venv venv
   venv/Scripts/activate
   

2. Install required dependencies using pip:

   pip install pandas nltk python-dotenv
   

3. Download necessary NLTK packages:

   python -m nltk.downloader vader_lexicon
   

4. Download Amazon food reviews dataset from Kaggle and place it in the appropriate directory

Running the Streamlit Interface

To run the Streamlit interface for sentiment analysis and summary generation:

1. Install Streamlit if not already installed:

   pip install streamlit
   

2. Run the Streamlit app:

   streamlit run streamlit_app.py
   

3. Enter customer feedback text in the input area and click "Analyze" to see sentiment and summary results.