The project addresses the challenge of extracting actionable insights from a vast amount of real-time news data.
By integrating natural language processing and network graph visualization, it empowers users to make informed decisions by understanding market trends and sentiment analysis.

describe what can people use it for, or how it makes existing tasks easier/safer e.t.c (markdown supported)

The problem it solves

1. Issues with scrapping news articles
2. Correctly identifying events and entities using NER.


tell us about any specific bug or hurdle you ran into while building this project. how did you get over it? (markdown supported)

Challenges we ran into

LOC 6.0

React

Firebase

spaCy

gemini

Syntaxa

The project focuses on "Real-Time Network Graph Generation from News Data."
Key components include:
Retrieval of news data from various sources.
Utilization of natural language processing (NLP) techniques for data analysis.
Implementation of the networkx library for generating interactive network graphs.
Designing an intuitive user interface for user interaction.
The system aims to assist users in making strategic decisions by providing insights into market trends and news events.
Sentiment analysis is performed to understand the impact of each event on individual companies.
The project emphasizes scalability and performance to handle real-time data effectively.
The goal is to offer users a comprehensive platform for analyzing market data and making informed decisions based on both quantitative data and sentiment analysis.

AI/ML

Byte Brigade

Syntaxa

Empowering Decision-Makers with Real-Time Insights from News Data

The problem Syntaxa solves

Challenges we ran into

AI/ML