You will get Crypto News Classification & Semantic Search

Project details
I will build a complete AI pipeline to analyze cryptocurrency news, detect sentiment trends, rank results using cross-encoders, and create a semantic vector search engine for fast market insights. You’ll get a Persian news classifier, sentiment analysis, cross-encoder ranking, audio news transcription via Whisper/Wav2Vec, automatic news clustering, and a Streamlit dashboard to monitor everything. All models are trained, evaluated, and deployed using state-of-the-art NLP and deep learning techniques.
Programming Languages
JavaScript, Python, TypeScriptCoding Expertise
Localization, Performance Optimization, SecurityWhat's included
| Service Tiers |
Starter
$150
|
Standard
$350
|
Advanced
$650
|
|---|---|---|---|
| Delivery Time | 4 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
Design Customization | - | ||
Content Upload | - | ||
Responsive Design | - | - | |
Source Code |
About Van
Data science & AI
Barnet, United Kingdom - 11:52 pm local time
My core strength lies in architecting clean, scalable APIs, optimizing data layers, and delivering production-grade machine learning systems—from embedding pipelines and vector search to RAG-enabled chatbots and high-performance microservices. I combine strong engineering fundamentals with practical cybersecurity and OSINT insight, enabling me to build systems that are not only fast and reliable, but also secure and resilient.
Steps for completing your project
After purchasing the project, send requirements so Van can start the project.
Delivery time starts when Van receives requirements from you.
Van works on your project following the steps below.
Revisions may occur after the delivery date.
Client sends dataset and requirement
You provide your news data (CSV, JSON, or links) and specify goals, labels, and any technical preferences.
Build & train classification and sentiment models
Develop Persian crypto news classifier and sentiment analysis models using LSTM, CNN, SVM, or deep learning approaches.