NLP: Keyword Extraction, Text Summarization, and Topic Modeling on Unlabeled Property Rental Listings
This repository explores the application of Natural Language Processing (NLP) techniques to unlock valuable insights from property data, specifically focusing on rental listings.
Code Implementation: Dive into practical code examples demonstrating techniques like text summarization, named entity recognition (NER), topic modeling, and text prediction on a property rental listing dataset.
Methodology Breakdown: A comprehensive Jupyter Notebooks will detail the step-by-step process of applying these NLP techniques, using libraries like spaCy and Scikit-Learn.
Real-World Use Case: A showcase how NLP can be used to analyze Airbnb rental listings in Tokyo, but the code is adaptable to other text datasets with minimal adjustments.
Uncover hidden patterns and trends within property descriptions. Extract key information using NER (e.g., location, amenities). Generate summaries to improve user experience and information retrieval. Explore topic modeling to identify clusters of similar properties. (Future) Implement text prediction for tasks like rental price estimation.
Follow Along & Experiment!
The codebase is open-source and available on GitHub for you to:
Fork the repository and experiment with the code.
Reproduce the results obtained on the Airbnb rental listings dataset.
Apply these NLP techniques to your own text data for various use cases.
Daniel Kristiyanto
Bali, Summer 2024
https://medium.com/@kristiyanto_