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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