Summary
An eclectic news search engine that helps with deduplication of news articles and enriches your reading experience.
Overview
An eclectic news search engine that cuts through the noise and allows readers to dig deep into a specific topic or explore multiple facets of the news they are most interested in.
Inspiration
The problem statement selected is ‘Reduce The Noise Of News Search’ (No.7). Time is of essence and hence spending it by only consuming necessary information is very important. That is the problem this project tries to solve in the domain of news search.
What it does
An eclectic news search engine that helps with deduplication of news articles and enriches your reading experience. Enables readers to dig deep into a certain opinion or explore multiple facets of the news at hand.
How we built it
- Fetched news articles from newsapicatcher.com.
- Used the PyTigerGraph Python library to interact with the TigerGraph instance.
- Used 3 different NLP models for Semantic Search, Keyword Generation and Sentiment Analysis respectively. Helps with enriching the news articles with additional metadata.
- Later, loaded all data to the TigerGraph instance and processed/explored data in several ways using custom GSQL queries as well as special algorithms like centrality and similarity.
- Finally, results are shown to the end-user in a Streamlit web application.
