Handling imbalanced text data in NLP - Model Pipeline Trace

Model Pipeline - Handling imbalanced text data

This pipeline shows how to handle imbalanced text data by balancing classes before training a text classifier. It uses simple text cleaning, converts text to numbers, balances classes with oversampling, trains a model, and tracks improvement.

Data Flow - 5 Stages

1Raw Text Data

1000 rows x 2 columns→Original dataset with text and labels, imbalanced classes→1000 rows x 2 columns

[{'text': 'I love this movie', 'label': 'positive'}, {'text': 'Bad experience', 'label': 'negative'}]

↓

2Text Cleaning

1000 rows x 2 columns→Lowercase, remove punctuation and stopwords→1000 rows x 2 columns

[{'text': 'love movie', 'label': 'positive'}, {'text': 'bad experience', 'label': 'negative'}]

↓

3Text Vectorization

1000 rows x 2 columns→Convert text to numeric vectors using TF-IDF→1000 rows x 5000 features

[[0,0,0.3,...,0.1], [0.2,0,0,...,0]]

↓

4Class Balancing

1000 rows x 5000 features→Oversample minority class to balance dataset→1400 rows x 5000 features

Balanced dataset with equal positive and negative samples

↓

5Model Training

1400 rows x 5000 features→Train logistic regression classifier→Trained model

Model ready to predict sentiment

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |    
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Starting training with balanced data
2	0.50	0.75	Loss decreased, accuracy improved
3	0.40	0.82	Model learning important patterns
4	0.35	0.85	Training converging well
5	0.32	0.87	Final epoch with good accuracy

Prediction Trace - 4 Layers

Layer 1: Input Text

Layer 2: Vectorization

Layer 3: Model Prediction

Layer 4: Class Decision

Model Quiz - 3 Questions

Why do we oversample the minority class in this pipeline?

A To speed up training

B To reduce the number of features

C To balance the number of samples in each class

D To remove noisy data

Key Insight

Balancing imbalanced text data by oversampling helps the model learn equally from all classes, improving accuracy and reducing bias toward majority classes.