What I did

• I Loaded Textual data and preprocess and clean the texts.
• I did Tokenization and inter encoding using Tokeinzer Class.
• I did Padding .
• I Trained an each model to Get good F1-score.
• I Visualized Results.
• Did Evaluations on Test Data.

About the Dataset:

Each entry in this dataset consists of a text segment representing a Twitter message and a corresponding label indicating the predominant emotion conveyed.

The emotions are classified into six categories: sadness (0), joy (1), love (2), anger (3), fear (4), and surprise (5).

Whether you're interested in sentiment analysis, emotion classification, or text mining, this dataset provides a rich foundation for exploring the nuanced emotional landscape within the realm of social media.

Download link

Key Features:

text: A string feature representing the content of the Twitter message.

label: A classification label indicating the primary emotion, with values ranging from 0 to 5.

Potential Use Cases:

Sentiment Analysis: Uncover the prevailing sentiments in English Twitter messages across various emotions.

Emotion Classification: Develop models to accurately classify tweets into the six specified emotion categories.

Textual Analysis: Explore linguistic patterns and expressions associated with different emotional states.

Sample Data:

text | label | |------------------------------------------------|-------|

that was what i felt when i was finally accept…| 1 |

i take every day as it comes i'm just focussin…| 4 |

i give you plenty of attention even when i fee…| 0 |

How to use

• Download Dataset and place it in the dataset folder (should result in ./dataset/text.csv)
• Run Model.ipynb and execute all the code cells to train each model
• Use the predictors.py module to create an instance of Predictor and load the model weights and its vectorizer into it
• Call the get_prediction(text: str) method to get prediction