Update README.md

README.md

@@ -1,3 +1,4 @@
+---
 title: Feedback Topic Sentiment Transformer
 emoji: 📊
 colorFrom: blue
@@ -6,60 +7,41 @@ sdk: gradio
 sdk_version: 4.19.2
 app_file: app.py
 pinned: false
-license: mit
-Feedback Topic & Sentiment Transformer
-This tool transforms feedback data with topic and sentiment columns into a binary matrix format where each unique topic becomes a separate column.
-Features
+---
 
-Multi-format Support: Accepts Excel (.xlsx, .xls), CSV, and tab-delimited text files
-Flexible Column Detection: Configurable prefixes for topic, sentiment, and category columns
-Binary Matrix Output: Transforms topics into columns with 0/1 values
-Sentiment Analysis: Associates sentiment scores (positive=1, neutral=0.5, negative=0) with each topic
-Data Analysis: Provides insights on topic frequency and sentiment distribution
-Download Options: Export results as Excel or CSV files
+# Feedback Topic & Sentiment Transformer
 
-How to Use
+Transform feedback data with topic and sentiment columns into a binary matrix format.
 
-Upload your data file containing feedback with topic and sentiment columns
-Configure column prefixes to match your data format:
+## Features
 
-Topic columns (e.g., [**WORKSHOP] SwissLife Taxonomy)
-Sentiment columns (e.g., ABSA:)
-Category columns (e.g., Categories:)
+- Upload Excel, CSV, or tab-delimited files
+- Configure column prefixes for topics, sentiments, and categories
+- Transform topics into binary columns (0/1)
+- Associate sentiment scores with each topic
+- Analyze topic frequency and sentiment distribution
+- Download results as Excel or CSV
 
+## Usage
 
-Click "Transform Data" to process your file
-Review the analysis (optional) to see topic frequencies and sentiment distributions
-Download the transformed file in your preferred format
+1. Upload your feedback data file
+2. Configure column prefixes
+3. Click "Transform Data"
+4. Download the transformed file
 
-Input Format
-Your input file should have columns structured like:
+## Input Format
 
-Topic columns: Contains the topic/category names
-Sentiment columns: Contains sentiment values (positive/negative/neutral)
-Text column: Original feedback text (optional)
-Recommendation column: Recommendation scores (optional)
+Your file should contain:
+- Topic columns (e.g., `[**WORKSHOP] SwissLife Taxonomy`)
+- Sentiment columns (e.g., `ABSA:Sentiment`)
+- Category columns (e.g., `Categories:`)
+- Text column (optional)
+- Recommendation column (optional)
 
-Output Format
-The transformed file will contain:
+## Output Format
 
-feedback_id: Unique identifier for each feedback row
-Topic columns: Binary (0/1) indicating topic presence
-Sentiment columns: Numeric sentiment scores for each topic
-original_text: Preserved from input (if available)
-recommendation_score: Preserved from input (if available)
-
-Example Use Case
-Perfect for transforming customer feedback data with multiple topics and sentiments into a format suitable for:
-
-Machine learning models
-Statistical analysis
-Topic modeling
-Sentiment trend analysis
-
-Technical Details
-Built with:
-
-Gradio for the web interface
-Pandas for data processing
-Support for Excel and CSV formats
+The transformed file includes:
+- `feedback_id`: Unique identifier
+- Topic columns: Binary values (0/1)
+- Sentiment columns: Scores (1=positive, 0.5=neutral, 0=negative)
+- Original text and recommendation scores (if available)