Crypto markets move at lightning speed, driven by global news, community sentiment, and collective emotion — not just price charts. Most traders struggle to keep up, relying on gut feelings or reacting too late.
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> ### The Problem
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> ### 🚨 The Problem
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**Signal-to-Noise Problem**
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### Signal-to-Noise Problem
> The crypto market thrives on constant online chatter — from influencers, news sources, and discussion forums — but manually analyzing this flood of information is nearly impossible.
> **Traders struggle to distinguish authentic market-moving insights from distractions** like scams, bots, and irrelevant commentary.
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**Lack of Quantified Sentiment**
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### Lack of Quantified Sentiment
> News-Based-Sentiment is one of the primary drivers of crypto volatility, yet most traders lack a reliable, data-driven way to measure it.
> There is **no objective, probabilistic tool** that links market conversation directly to actionable price predictions, leaving traders to rely on **intuition instead of insight**.
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**Architecture Diagram:**
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**Sequence Diagram:**
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**Flow chart Diagram:**
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> ## HLD
[Sentify](./Sentify.pdf)
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What was the hardest part of this project? What did your team learn about the AI models, the data, or the problem domain?
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> ## Future Roadmap
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What are the next steps for this project if it were to continue?
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* [ ] Short-term goal (e.g., Improve model accuracy to 95%)
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* [ ] Medium-term goal (e.g., Launch a mobile app)
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* [ ] Long-term goal (e.g., Open-source the project)
> ## Repository & Live Demo
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Coming soon.....
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* **GitHub Repository:** [Link to your code repo here]
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* **Live Demo:** [Link to your live demo or video walkthrough here] (Highly encouraged!)
> ## Screenshots / Demo Video
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Coming soon....
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(Embed screenshots of your working application or a video demo here. A picture is worth a thousand words.)