You will get I will build an AI sentiment analysis API with Python
Project details
I will build an AI-powered Sentiment Analysis API using FastAPI and Google Cloud NLP. The API will analyze text and return sentiment scores (positive, negative, neutral) as well as emotional tags (joy, sadness, anger). It will be secured with JWT authentication, documented with Swagger UI, and delivered with clear deployment instructions (local or Docker). This service is ideal for apps, chatbots, or customer feedback systems that need accurate and scalable text analysis.
Machine Learning Tools
Google AutoML, NLTK, NumPy, OpenCV, Python, scikit-learn, SQL, TensorFlow, TextBlobWhat's included
| Service Tiers |
Starter
$120
|
Standard
$250
|
Advanced
$450
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 8 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 1 | 2 |
Number of Scenarios | 1 | 2 | 3 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
About Ahmet
iOS & AI App Developer | Swift, Firebase, FastAPI
Kayseri, Turkey - 3:54 am local time
What I Can Build for You
*iOS Apps (Swift & SwiftUI)*
* Firebase Auth (Email and Google Sign-In)
* RevenueCat subscriptions
* Multi-language support and localization
* Secure local storage and modern UI/UX
* App Store deployment
*Published iOS App – StylePath*
I developed and published StylePath, an AI-powered fashion application available on the App Store. You can view it in my Upwork Portfolio below.
*AI and Machine Learning*
* Custom model training and fine-tuning
* Sentiment and mood analysis
* ChatGPT and Google NLP assistants
* Computer vision with OpenCV, YOLO and MediaPipe
* Predictive modeling
* CoreML edge deployment
*Brain Tumor MRI Classification Project*
I independently trained and evaluated multiple deep-learning models for classifying brain MRI images into glioma, meningioma, pituitary tumor and no-tumor categories.
* Trained EfficientNet-B0, EfficientNet-B1, EfficientNet-B2 and MobileNetV3-Large models
* Applied transfer learning, fine-tuning, data augmentation and class weighting
* Used Focal Loss and threshold optimization to improve glioma detection
* Evaluated models with confusion matrices, precision, recall and F1-score
* Converted the selected model to CoreML for integration into a SwiftUI application
* Developed a mobile workflow for selecting MRI images and displaying AI-generated classification results
*Backend APIs (FastAPI / Flask)*
* JWT authentication
* PostgreSQL, MySQL and MongoDB integration
* Async processing with Celery, RabbitMQ and Kafka
* CI/CD pipelines and Dockerized deployments
*Cloud & DevOps*
* Firebase Functions
* Scalable architecture on Google Cloud and AWS
* GitHub Actions
Why Clients Work With Me
✔ Published iOS application on the App Store
✔ Hands-on experience training custom AI models
✔ Clear communication and well-documented code
✔ Fast delivery with a startup mindset
✔ Focus on secure and scalable solutions
✔ Strong foundation in AI, mobile and backend development
✔ Available for long-term support
Let’s bring your idea to life.
Message me today, and I’ll suggest the best approach — I can even prepare a quick demo for your project.
Steps for completing your project
After purchasing the project, send requirements so Ahmet can start the project.
Delivery time starts when Ahmet receives requirements from you.
Ahmet works on your project following the steps below.
Revisions may occur after the delivery date.
Backend Setup
I set up the FastAPI project with required dependencies and environment.
NLP Integration
I integrate Google Cloud NLP to process text and return sentiment/emotion results.