GeoSentiment Lens-Interactive Sentiment & Geo-Mapping

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  Sentiment Timeline analysis with insights.

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  Live feed option to fetch real time data from X and other resources.

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  A small interactive game.

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  Smart insights obtained.

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  The dashboard providing the in depth analysis.

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  The Geographic map showing the sentiment analysis in various regions.

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  The Landing Page where user can either upload a dataset or use live data fetching option.

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  The Opening Page where the user is given information about the website.

Inspiration

Understanding how public sentiment shapes global narratives has become more critical than ever. I wanted to build a platform that doesn't just process text sentiment but shows how emotions, opinions, and trends evolve geographically and over time. Existing tools often lacked either real-time data, clear geographic visualization, or accessibility — that gap inspired me to create GeoSentiment Lens.

What it does

GeoSentiment Lens is an interactive, AI-powered platform that:

• Analyzes real-time sentiment from social media and news sources.
• Maps global sentiment patterns using Leaflet, an interactive world map library, with emojis, markers, and heatmaps.
• Detects emotions like Joy, Sadness, Anger, Fear, and Surprise.
• Shows sentiment trends over time through interactive timelines.
• Tracks keyword and hashtag trends globally.
• Provides a comprehensive dashboard to visualize sentiment scores, emotion breakdown, trend graphs, and data insights.
• Allows users to upload custom datasets and generate sentiment insights.
• Fetches live data using Twitter API and News API integrations.

How we built it

• The app was built entirely using Bolt AI, leveraging its no-code capabilities for rapid development.
• Supabase was integrated for secure, persistent dataset storage and user authentication.
• Twitter API and News API were connected on the backend to fetch live data.
• Prompt engineering within Bolt was used to automate logic for sentiment analysis and data visualization.
• Sentiment and emotion detection are handled using AI models integrated through Bolt, with processing logic secured in the backend.
• Leaflet was used to build an interactive, zoomable, global map that displays sentiment patterns visually.
• The dashboard provides real-time summaries, graphs, and interactive components for easy sentiment monitoring.

Challenges we ran into

• Configuring live API integrations and ensuring smooth real-time updates.
• Ensuring dataset uploads persist in the system rather than disappearing after refresh.
• Handling accurate sentiment and emotion detection without overcomplicating the user interface.
• Managing map markers, heatmaps, and emojis to reflect live and analyzed data properly on the Leaflet map.
• Structuring prompt instructions to make Bolt perform exactly as intended.
• Designing a dynamic and intuitive dashboard that updates with real-time and processed data.
• Navigating API rate limits, especially with Twitter's free tier.

Accomplishments that we're proud of

• Successfully built a fully functional, real-time sentiment and geo-mapping platform without extensive manual coding.
• Integrated live Twitter and news data into the system securely through backend logic.
• Enabled users to upload their own datasets and see meaningful sentiment analysis.
• Implemented an interactive world map using Leaflet for geographic sentiment visualization.
• Designed a comprehensive dashboard with real-time charts, graphs, and emotion breakdowns.
• Learned how to leverage prompt engineering to control app logic effectively within Bolt.

What we learned

• How powerful no-code platforms like Bolt AI can be when combined with good backend planning.
• Effective use of Supabase for data storage and authentication.
• How to integrate external APIs for live data processing.
• Using Leaflet to build interactive, zoomable maps for geographic data visualization.
• The importance of structuring prompts clearly to guide AI behavior.
• Basics of real-time data handling, dashboard design, and interactive visualizations.
• Practical challenges of working with live social media data and news feeds.

What's next for GeoSentiment Lens - Interactive Sentiment & Geo-Mapping

• Expanding to support additional live data sources beyond Twitter and News API.
• Improving emotion detection with more advanced AI models.
• Adding deeper filtering options for the map and dashboard.
• Enhancing timeline visualizations to show more detailed trends.
• Optimizing performance for large datasets.
• Adding advanced features to the dashboard like comparison charts and user-customizable reports.
• Exploring mobile-friendly versions for on-the-go sentiment tracking.
• Making the platform accessible to non-technical users through further UI improvements.

Built With

• and-lucide-react-for-icons.-development-tools:-vite-as-the-build-tool-and-development-server
• and-sql.-frontend-framework/libraries:-react-for-the-user-interface
• css
• eslint
• eslint-for-code-linting
• html
• javascript
• leaflet-and-react-leaflet-for-interactive-maps
• leaflet.js
• luicde-react
• postcss
• postgresql
• react
• react-leaflet
• sql
• supabase
• tailwind
• tailwind-css-for-styling
• typescript
• vite