SentiAn

Inspiration

SENTIAN was inspired by the need to help users quickly understand and gain insights from the vast amount of news content available online. The information overload in today's digital age makes it challenging for individuals to stay informed about topics they care about without spending hours reading multiple articles. SENTIAN aims to solve this problem by providing rapid, comprehensive analysis of news sentiment and content.

What it does

SENTIAN analyzes the 100 most recent news articles on a user-specified topic in real-time. It performs sentiment analysis to categorize articles as positive, negative, or neutral, generates concise summaries of key points across all articles, and creates word clouds to visually represent frequently used terms for both positive and negative sentiments. This allows users to quickly grasp the overall sentiment and main themes surrounding their topic of interest.

How we built it

SENTIAN likely utilizes natural language processing (NLP) and machine learning algorithms to perform sentiment analysis and text summarization. The app probably integrates with various news APIs to fetch real-time articles. For the frontend, it uses Python dash app and python anyhwere deployment tool to create an interactive user interface. The backend built with Python, using libraries like NLTK,wordcloud, googlenews API, Regular expression- RE.

Challenges we ran into

Ensuring accurate sentiment analysis across diverse writing styles and topics Developing efficient algorithms to process 100 articles in real-time Creating meaningful summaries that capture key points from multiple sources Designing an intuitive user interface to display complex data simply

Accomplishments that we're proud of

Successfully processing and analyzing 100 articles in real-time Developing a user-friendly interface that presents complex data in an easily digestible format Creating an app that can potentially save users hours of reading time while keeping them well-informed Deployment using pythonanywhere.

What we learned

The team likely gained insights into advanced NLP techniques, real-time data processing, and effective data visualization methods. They may have also learned about the challenges of working with diverse news sources and the importance of user experience design in presenting complex information.

What's next for SENTIAN

Future developments for SENTIAN could include:

Expanding language support for international news analysis Implementing user customization options for analysis preferences Developing a mobile app version for on-the-go news insights Integrating with social media platforms to analyze public sentiment on topics Adding features for tracking sentiment changes over time on specific topics