Twitter Sentiment Analysis with TF-IDF and Random Forest Model
Watch YouTube Video Here: https://youtu.be/U1-7VIXnSs8
📺 Welcome to NLP Projects 3! In this video, we dive into the exciting world of Twitter Sentiment Analysis using Random Forest and a sleek Streamlit App. 🌐🌟
🔍 Discover how we harness the power of Natural Language Processing to analyze tweets and uncover sentiments. 💬🤖
🌲 Learn how Random Forest, a robust machine learning algorithm, helps us classify tweets into positive, negative, or neutral categories with accuracy. 📈🌟
🚀 We'll also showcase our user-friendly Streamlit App, making the analysis accessible to all. 📱💻
Don't miss out on this fascinating project! Tune in now and level up your NLP skills. 📊🔥 #NLP #SentimentAnalysis #MachineLearning #StreamlitApp #TwitterAnalysis
| ML Course | Description |
|---|---|
| Data Visualization in Python Masterclass™: Beginners to Pro | Learn to build Machine Learning and Deep Learning models using Python and its libraries like Scikit-Learn, Keras, and TensorFlow. |
| Python for Machine Learning: A Step-by-Step Guide | Learn to build Machine Learning and Deep Learning models using Python and its libraries like Scikit-Learn, Keras, and TensorFlow. |
| Python for Linear Regression in Machine Learning | Learn to build Linear Regression models using Python and its libraries like Scikit-Learn. |
| Introduction to Spacy 3 for Natural Language Processing | Learn to build Natural Language Processing models using Python and its libraries like Spacy. |
| Advanced Machine Learning and Deep Learning Projects | Learn to build Advanced Machine Learning and Deep Learning models using Python and transformer models like BERT, GPT-2, and XLNet. |
| Natural Language Processing in Python for Beginners | Learn to build Natural Language Processing Projects using Spacy, NLTK, and Gensim, and transformer models like BERT, GPT-2, and XLNet. |
| Deployment of Machine Learning Models in Production in Python | Learn to deploy Machine Learning and Deep Learning models using Python and its libraries like Flask, Streamlit, and NGINX. |
| R 4.0 Programming for Data Science - Beginners to Pro | Learn to build Machine Learning and Deep Learning models using R and its libraries like caret, tidyverse, and keras. |