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.
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.
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.
Workflow Optimization
Integrate a Feedback Loop Square to collect and label misclassified samples for continuous improvement.
Blockchain Traceability
Enable blockchain logging on the Ethereum network to track data provenance and workflow integrity.
Visualize and Export Results
Use an Output Square to display sentiment scores and export the results for external reporting tools.
Last updated