Problem: News media often amplifies negativity due to its attention-grabbing nature, leading to negativity bias among consumers. This can contribute to anxiety, cynicism, and a distorted view of the world.
Solution: Video-based news aggregator tackles this by leveraging cloud infrastructure, sentiment analysis, and video creation to deliver a balanced and informative news experience..
Key Features:
Scours the web: Leverages cloud infrastructure to scan a wide range of news sources for both positive and negative stories.
Sentiment analysis: Employs a Machine Learning model to assess the sentiment of each story, identifying positivity within seemingly negative news.
Positive impact focus: Highlights the positive aspects or silver linings within negative stories, providing a more balanced and constructive perspective.
Personalized video digest: Based on user preferences and sentiment analysis, the system automatically curates and joins relevant news stories into concise video summaries.
Informative value: Maintains journalistic integrity by ensuring the selected stories remain informative and factual.
Delivery channel: Delivers personalized video digests directly to users' email.
Technical Skills:
Software engineering: Building the platform, integrating the sentiment analysis model, and managing cloud infrastructure.
Machine learning & AI: Training and refining the sentiment analysis model for accurate positive sentiment identification, and potentially exploring natural language processing for video summarization.
Project Goals:
Develop a reliable system for identifying positive aspects within negative news.
Design an engaging and informative video digest format for subscribers.
Personalize the news experience based on user preferences
Design an engaging and informative video digest format for subscribers.
Promote a more balanced and optimistic view of the world.