metadata

title: BulkSentimentAnalysis
app_file: app.py
sdk: gradio
sdk_version: 5.5.0

Bulk Sentiment Analysis for Reviews

This project provides a tool for bulk sentiment analysis of store reviews. Using various sentiment analysis models, including VADER and Hugging Face's Transformers, users can analyze reviews in CSV or Excel format to understand the overall sentiment. Results can be downloaded in CSV format, making it easy to leverage insights for decision-making.

Features

Bulk Upload: Upload CSV or Excel files of reviews for analysis.
Model Options: Choose from multiple sentiment analysis models:
- VADER for rule-based sentiment analysis.
- SpaCy (Static Embeddings) and SpaCy (Contextual Embeddings) for improved flexibility (coming soon).
- Transformers for deep learning-based sentiment analysis using pretrained models from Hugging Face.
Download Results: Export analyzed reviews with sentiment scores and labels as a CSV.

Installation

To get started, clone this repository and install the dependencies.

git clone https://github.com/yourusername/bulk-sentiment-analysis
cd bulk-sentiment-analysis
pip install -r requirements.txt

Ensure your environment supports GPU processing, especially for Transformer-based models, to handle large datasets more efficiently.

Dependencies

Python 3.7+
Streamlit for building the user interface
Pandas for data handling
NLTK and VADER for sentiment analysis
SpaCy (optional, for static and contextual embedding-based analysis)
Transformers by Hugging Face for deep learning sentiment models

To install these, you can run:

pip install streamlit pandas nltk torch transformers spacy

For GPU support, install the appropriate CUDA version of PyTorch, following instructions from the official PyTorch website.

Usage

Run the Application

Launch the Streamlit app with:
```
streamlit run app.py
```
Upload Your File

Upload a CSV or Excel file containing reviews. Make sure there is a column labeled Review with the text you want to analyze.
Choose a Model

Select one or more sentiment analysis models:
- VADER for quick, rule-based analysis.
- Transformers for more accurate, context-based sentiment detection.
Analyze and Download Results

After processing, the app will display results in the UI. You can download the full results as a CSV file.

Example of CSV Output

The output CSV will contain the original reviews alongside new columns with sentiment scores and labels, depending on the models chosen.

Folder Structure

app.py: Main Streamlit application script.
sentiment_analysis/: Contains modular functions for each sentiment analysis model (e.g., vader_analyzer.py, spacy_static.py, etc.).
requirements.txt: List of required dependencies.

Future Enhancements

Integrate SpaCy models for static and contextual embedding-based sentiment analysis.
Improved preprocessing to handle non-English text and non-standard review formats.
Performance optimization with caching and batch processing for large datasets.

Contributing

Feel free to open issues or submit pull requests for improvements. All contributions are welcome!

This README should give users a clear understanding of what the project does, how to set it up, and how to use it. Let me know if you'd like any more specific instructions or additional sections!