This repository contains a Python notebook for performing emotion recognition on tweets using TensorFlow. The notebook includes steps for importing the data, tokenizing the tweets, preparing the data for training, building and training a Bidirectional LSTM model, evaluating the model's performance, and visualizing the results.

To run the notebook, you need to install the required libraries. You can install them via pip:

pip install nlp
pip install datasets

1. Importing the Tweet Emotion dataset.
2. Creating train, validation, and test sets.
3. Extracting tweets and labels from the examples.

1. Tokenizing the tweets.

1. Checking the length of the tweets.
2. Creating padded sequences.

1. Creating classes to index and index to classes dictionaries.
2. Converting text labels to numeric labels.

1. Creating the model architecture.
2. Compiling the model.

1. Preparing a validation set.
2. Training the model.

1. Visualizing training history.
2. Preparing a test set.
3. A look at individual predictions on the test set.
4. A look at all predictions on the test set.

To run the notebook, simply open it in a Jupyter Notebook environment or any compatible platform.

This project is licensed under the MIT License.