Tweet-Emotion-Recognition
In this project, we're going to create a recommend neural network and create it on a tweet emotion data set to learn to recognize emotions in tweets. The data set has thousands of tweets, each classified in one off six emotions. We will take a closer look at the examples and labels in a few minutes is a multi class classification problem in the natural language processing domain. We will be using TensorFlow as our machine learning framework. The project uses the Google Collab environment, which is a fantastic tool for creating and running Jupiter notebooks in the cloud. You will need prior programming experience in Python.This is a practical hands-on guided project for learners who already have theoretical understanding off. Neural networks recommend neural networks and optimization algorithms like creating dissent, but I want to understand how to use TensorFlow to start performing natural language processing tasks like text classification.
Alright, so that's enough for the introduction. Let’s continue to the next task and start working on this project.
Emotion is a dataset of English Twitter messages with six basic emotions:
| Emotion |
label |
Sadness |
0 |
Joy |
1 |
Love |
2 |
Anger |
3 |
Fear |
4 |
Surprise |
5 |
I'm a data scientist with a specialization in Natural Language Processing (NLP). I have experience working on NLP projects and conducting research in this field.
As an NLP researcher, I have expertise in a variety of NLP techniques such as text classification, sentiment analysis, named entity recognition, and text summarization.
