This repository contains an example of applying topic modeling on notes from NeoReader and Readwise, as well as visualizing the resulting word clouds. Topic modeling is a technique used in natural language processing (NLP) to discover hidden patterns and semantic structures within a collection of documents. It is an unsupervised machine learning method that helps organize and understand large collections of text data.
A word cloud is a visual representation of text data where the size of each word indicates its frequency or importance in the document. It helps in identifying key themes and patterns in the text.
The topic_modeling.ipynb Jupyter notebook demonstrates the application of topic modeling on notes exported from NeoReader and Readwise. The notes from NeoReader have been exported and added to the folder data/boox_exports, while the notes from Readwise have been exported and added to the folder data/readwise.
To get started with the notebook, follow these steps:
- Clone the repository.
- Run the Jupyter notebook using jupyter notebook.
- Open topic_modeling.ipynb in the Jupyter notebook interface and execute the cells.
The code in the topic_modeling.ipynb notebook generates an interactive HTML visualization of the topics discovered in the text data. The visualization allows you to explore the relationships between the topics and the words that make up those topics.
This project is licensed under the MIT License - see the LICENSE file for details.