haydenpham/6emotions-classifier

6 Emotions Text Classification Model

A logistic regression model for classifying text into 6 emotion categories.

Model Description

• Model type: Logistic Regression with TF-IDF features
• Language: English
• Task: Multi-class text classification
• Labels: anger, fear, joy, love, sadness, surprise

Training Data

This model was trained on a merged dataset from two sources:

1. GoEmotions (Google): A corpus of 58k Reddit comments with 27 emotion categories

   • Source: Kaggle
   • Paper: arXiv:2005.00547

2. Emotion Dataset: Text samples labeled with basic emotions

   • Source: Kaggle
   • Paper: EMNLP 2018

Labels were mapped to 6 core emotion categories for this model.

Features

The model uses a combination of:

• Word-level TF-IDF: unigrams to trigrams (max 20,000 features)
• Character-level TF-IDF: 3-5 character n-grams (max 15,000 features)

Training

• Framework: scikit-learn
• Hyperparameter tuning: GridSearchCV with 3-fold cross-validation
• Class balancing: class_weight='balanced'

Performance

Model Metrics

• Cross-Validation Accuracy: 0.7163
• Test Accuracy: 0.70
• Training Size: 41,974
• Test Size: 6,067

Confusion Matrix

Limitations

• Trained on English text; performance on other languages is not guaranteed.
• May not generalize well to formal and technical texts.
• Single-label classification (no multi-emotion detection).
• Potential biases from training data sources.

Usage

import skops.io as sio

# Load model (review untrusted types before loading)
trusted_types = [
    "sklearn.pipeline.Pipeline",
    "sklearn.linear_model._logistic.LogisticRegression",
    "sklearn.feature_extraction.text.TfidfVectorizer",
    "numpy.ndarray",
    "numpy.dtype"
]

model = sio.load("6emotions_model.skops", trusted=trusted_types)

# Predict
text = "I'm so happy today!"
prediction = model.predict([text])
print(prediction)  # ['joy']