Go-Mood-Marker is an application designed to enrich text-based conversation datasets with sentiment analysis and other metadata annotations. This tool is essential for anyone looking to improve the quality of training datasets for language models (LLMs), particularly for applications involving conversational AI, sentiment understanding, and context-aware response generation.
- Sentiment Analysis: Automatically assesses and tags the emotional tone of each sentence in a conversation.
- Metadata Annotations: Enriches dataset with various metadata, including intent, topic, urgency level, and more.
- Customizable Annotations: Ability to define and include custom metadata based on specific project requirements.
- Data Preprocessing: Offers functionalities to clean and prepare text data for analysis.
- Extensible: Designed to integrate with various NLP APIs and tools for enhanced analysis.
- Golang (latest version recommended)
- Access to external NLP services for advanced sentiment analysis (optional)
- Clone the repository:
git clone https://github.com/yourusername/go-mood-marker.git
- Navigate to the project directory:
cd go-mood-marker- Install the necessary Go packages:
go get ./...- Prepare your conversation dataset in a readable format (e.g., CSV, JSON).
- Configure the application settings (API keys, custom annotation rules).
- Run the application:
go run main.go4 The output will be a dataset enriched with sentiment and other annotations.
We welcome contributions to Go-Mood-Marker! Please see CONTRIBUTING.md for how to get started.
This project is licensed under the MIT License - see the LICENSE.md file for details.
A shoutout to the open-source libraries and APIs that make this project possible. Contributors and community members who have provided feedback and suggestions.