I will build nlp for sentiment analysis and text classification

N
nadasalem543
N
nadasalem543
Nada Salem
build nlp for sentiment analysis and text classification
build nlp for sentiment analysis and text classification
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About this gig

I will develop high-quality NLP models for Sentiment Analysis and Text Classification using your text data. This Gig covers all essential stages of NLP projects, including:


Data preprocessing: cleaning, tokenization, and preparing text for modeling


Model training: building robust models with Python, TensorFlow, Keras, PyTorch, SpaCy, and NLTK


Model evaluation: reporting metrics like accuracy, F1-score, and classification reports


Deployment (optional): creating an interactive Streamlit Web App so you can easily test and use your model



You will receive ready-to-run Python scripts, Jupyter Notebooks, or fully functional Web Apps, with clear instructions for use.


This Gig is perfect for clients who need reliable, scalable, and fully documented NLP solutions for small, medium, or large datasets, whether for prototyping, research, or integration into applications.

  • Application Type

    • Web Application

  • Desktop Frameworks

    • Other

  • AI Type

    • Other

  • Programming Language

    • Python

  • Web Frameworks

    • Other

  • No & Low-Code Builders

    • Other

Get to know Nada Salem

Nada Salem
  • FromEgypt
  • Member sinceOct 2025

  • Languages

    English, Arabic
I’m an AI and Machine Learning developer with hands-on experience in Python, data analysis, and model deployment. I’ve worked on projects including: 1.Bank Loan Default Predictor (Machine Learning):predicting customer eligibility. 2.Real vs Fake Image Detection & Handwritten Character Recognition (Computer Vision):trained CNN and YOLO models with TensorFlow and OpenCV. 3.Twitter Sentiment Analysis & Spam Detection (NLP):processed text with SpaCy and NLTK. 4.Gender Voice Classifier (Audio AI):analyzed audio signals using Librosa and deep learning. I’ve also built an RFID attendance system .

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