FEEDBACK

A Flask-based web application that analyzes user comments using sentiment analysis, similarity detection, and AI-powered insights.

%%{init: {'theme': 'default', 'themeVariables': { 'fontSize': '16px'}, "securityLevel": "loose"}}%%
graph LR
    A[CSV Upload] --> B[CommentCleaner]
    B --> C[Data Processing]
    C --> D{Analysis Type}
    D -->|Sentiment Analysis| E[SentimentAnalyzer]
    D -->|Similarity Check| F[Sentence Transformer]
    D -->|Custom Query| G[LLM Analysis]
    E --> H[Results Dashboard]
    F --> I[Deduplicated Comments]
    I --> G
    G --> H

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Features

• CSV Upload: Process comment data from CSV files
• Sentiment Analysis: Analyze comment sentiment using TextBlob
• Similarity Detection: Remove duplicate or highly similar comments using sentence transformers
• AI-Powered Insights: Generate detailed insights and suggestions using LLM integration
• Interactive Query Interface: Ask specific questions about your comment data

Installation

1. Clone the repository:

git clone https://github.com/yourusername/comment-analysis-tool.git
cd comment-analysis-tool

2. Install dependencies:

pip install -r requirements.txt

3. Create a .env file with your configuration:

OPENAI_API_KEY=your_api_key_here

Dependencies

• Flask
• python-dotenv
• TextBlob
• sentence-transformers
• scikit-learn
• pandas
• numpy
• markdown

Usage

1. Start the Flask server:

python app.py

2. Navigate to http://localhost:5000 in your web browser

3. Upload a CSV file containing a 'comment' column

4. Analyze your comments using the available features:

   • View sentiment distribution
   • Generate AI insights
   • Ask specific questions about your data

API Endpoints

POST /upload_csv

Upload a CSV file containing comments for analysis.

POST /analyze

Analyze uploaded comments with customizable parameters:

• max_comments: Maximum number of comments to analyze (default: 1000)
• similarity_threshold: Threshold for detecting similar comments (default: 0.85)

POST /send_message

Ask specific questions about your comment data.

Project Structure

├── app.py                 # Main Flask application
├── templates/
│   └── index.html        # Frontend interface
├── requirements.txt       # Python dependencies
└── .env                  # Environment variables

Key Components

CommentCleaner

• Removes HTML tags and non-ASCII characters
• Ensures clean text input for analysis

SentimentAnalyzer

• Calculates sentiment polarity using TextBlob
• Provides sentiment distribution statistics

InsightGenerator

• Removes similar comments using cosine similarity
• Generates AI-powered insights using LLM integration