# Creating a Custom NLP Workflow for Sentiment Analysis

**Objective:** Design a natural language processing (NLP) pipeline for sentiment classification using drag-and-drop modular squares.

1. **Data Preparation**
   * Import text data using a **Data Connector Square** (e.g., CSV, JSON, or API input).
   * Add a **Text Preprocessing Square** to clean the data by removing stop words, tokenizing, and stemming.
2. **Model Selection and Training**
   * Incorporate a **Pre-Trained NLP Model Square** (e.g., BERT or GPT-based) for sentiment analysis.
   * Configure the model for sentiment labels (e.g., positive, neutral, negative) and fine-tune on specific datasets if needed.
3. **Workflow Optimization**
   * Integrate a **Feedback Loop Square** to collect and label misclassified samples for continuous improvement.
4. **Blockchain Traceability**
   * Enable blockchain logging on the Ethereum network to track data provenance and workflow integrity.
5. **Visualize and Export Results**
   * Use an **Output Square** to display sentiment scores and export the results for external reporting tools.


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