Multi-class text classification in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is multi-class text classification?

It is a task where a text is sorted into one of many possible categories. For example, sorting emails into 'work', 'personal', or 'spam'.

intermediate

Why do we use softmax activation in multi-class classification models?

Softmax turns model outputs into probabilities that add up to 1, helping the model pick the most likely class.

beginner

Name a common loss function used for multi-class text classification.

Cross-entropy loss is commonly used because it measures how close the predicted probabilities are to the true class labels.

beginner

How does tokenization help in text classification?

Tokenization breaks text into smaller pieces like words or subwords, making it easier for the model to understand and learn from the text.

intermediate

What metric would you use to evaluate a multi-class text classification model?

Accuracy is common, but also precision, recall, and F1-score per class help understand model performance better.

What does multi-class text classification predict?

A Only two categories

B Multiple labels at once

C One label from multiple possible categories

D No labels, just text generation

Multi-class classification assigns one label from many possible categories to each text.

Which activation function is typically used in the output layer for multi-class classification?

A ReLU

B Tanh

C Sigmoid

D Softmax

Softmax converts outputs into probabilities that sum to 1, suitable for multi-class tasks.

What is the purpose of cross-entropy loss in multi-class classification?

A Measures distance between words

B Measures difference between predicted and true class probabilities

C Calculates accuracy

D Normalizes input text

Cross-entropy loss measures how close predicted probabilities are to the actual class labels.

Which step comes first in preparing text for classification?

A Tokenization

B Evaluation

C Model training

D Prediction

Tokenization breaks text into pieces so the model can process it.

Which metric gives a balanced view of precision and recall in multi-class classification?

A F1-score

B Softmax

C Loss

D Accuracy

F1-score balances precision and recall, useful for understanding model performance.

Explain the main steps involved in building a multi-class text classification model.

Think about how raw text becomes a prediction.

Describe why softmax activation and cross-entropy loss work well together in multi-class classification.

Focus on probability and loss relationship.

Practice

1. What is the main goal of multi-class text classification?

easy

A. To sort text into multiple categories based on content

B. To translate text into another language

C. To count the number of words in a text

D. To generate new text from a given input

Solution

1. Step 1: Understand the task of multi-class text classification

   This task involves assigning each text sample to one category out of many possible categories.

2. Step 2: Compare options with the task definition

   Only To sort text into multiple categories based on content describes sorting text into multiple categories, which matches the task.

3. Final Answer:

   To sort text into multiple categories based on content -> Option A

4. Quick Check:

   Multi-class classification = sorting into many categories [OK]

Hint: Multi-class means sorting text into many groups [OK]

Common Mistakes:

• Confusing classification with translation
• Thinking it counts words instead of categorizing
• Mixing generation with classification

2. Which of the following is the correct way to represent text data for multi-class classification?

easy

A. Converting text into numerical vectors like TF-IDF or embeddings

B. Using raw text strings directly as input to the model

C. Sorting text alphabetically before training

D. Removing all punctuation and spaces only

Solution

1. Step 1: Identify how models process text

   Models cannot understand raw text strings; they need numbers to learn patterns.

2. Step 2: Check which option converts text to numbers

   Converting text into numerical vectors like TF-IDF or embeddings mentions converting text into numerical vectors like TF-IDF or embeddings, which is correct.

3. Final Answer:

   Converting text into numerical vectors like TF-IDF or embeddings -> Option A

4. Quick Check:

   Text must be numbers for models [OK]

Hint: Models need numbers, not raw text, to learn [OK]

Common Mistakes:

• Feeding raw text directly to models
• Thinking sorting text helps classification
• Ignoring the need for numerical representation

3. Given the following Python code snippet for multi-class text classification, what will be the output of print(predicted_class)?

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

texts = ["I love cats", "Dogs are great", "I hate rain"]
labels = ["positive", "positive", "negative"]

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)

model = MultinomialNB()
model.fit(X, labels)

new_text = ["I love dogs"]
X_new = vectorizer.transform(new_text)
predicted_class = model.predict(X_new)[0]

medium

A. "neutral"

B. "negative"

C. An error because of unknown words

D. "positive"

Solution

1. Step 1: Understand training data and labels

   The model is trained on texts labeled as "positive" or "negative". "I love cats" and "Dogs are great" are positive, "I hate rain" is negative.

2. Step 2: Predict class for new text "I love dogs"

   The new text contains words "I", "love", and "dogs" which appear in positive examples. The model predicts "positive" as the class.

3. Final Answer:

   "positive" -> Option D

4. Quick Check:

   New text matches positive words, so prediction is positive [OK]

Hint: New text similar to positive examples predicts positive [OK]

Common Mistakes:

• Assuming unknown words cause errors
• Choosing negative because of 'dogs' only
• Picking neutral which is not a trained label

4. Identify the error in this multi-class text classification code snippet:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression

texts = ["happy day", "sad night", "joyful morning"]
labels = ["positive", "negative", "positive"]

vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(texts)

model = LogisticRegression()
model.fit(texts, labels)

medium

A. Labels should be numbers, not strings

B. Passing raw texts instead of vectorized data to model.fit

C. Using LogisticRegression instead of Naive Bayes

D. TfidfVectorizer cannot be used with LogisticRegression

Solution

1. Step 1: Check input to model.fit()

   The model.fit() method expects numerical features, but raw texts are passed instead of vectorized data.

2. Step 2: Identify correct input

   The vectorized data X should be passed to model.fit, not the original texts.

3. Final Answer:

   Passing raw texts instead of vectorized data to model.fit -> Option B

4. Quick Check:

   Model needs numbers, not raw text, for training [OK]

Hint: Model.fit needs vectorized data, not raw text [OK]

Common Mistakes:

• Passing raw text directly to model.fit
• Thinking label type causes error here
• Believing vectorizer choice causes this error

5. You have a dataset with 5 classes and highly imbalanced text samples per class. Which approach best improves multi-class classification performance?

hard

A. Use only the most frequent class for prediction

B. Ignore imbalance and train model on raw data

C. Use class weighting or oversampling to balance training data

D. Remove classes with fewer samples to simplify problem

Solution

1. Step 1: Understand class imbalance impact

   Imbalanced classes cause models to favor majority classes, reducing accuracy on minority classes.

2. Step 2: Identify best practice to handle imbalance

   Using class weighting or oversampling balances the training data, helping the model learn all classes better.

3. Final Answer:

   Use class weighting or oversampling to balance training data -> Option C

4. Quick Check:

   Balance data to improve multi-class model accuracy [OK]

Hint: Balance classes with weighting or oversampling [OK]

Common Mistakes:

• Ignoring imbalance and expecting good results
• Removing minority classes loses valuable data
• Predicting only the majority class ignores others