Fine-grained sentiment (5-class) in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Model Choice

intermediate

Choosing the best model for 5-class sentiment classification

You want to classify movie reviews into 5 sentiment classes: very negative, negative, neutral, positive, very positive. Which model is best suited for this task?

A A regression model predicting a continuous sentiment score from 1 to 5.

B A binary logistic regression model trained to predict positive vs negative sentiment.

C A clustering algorithm like K-means to group reviews into 5 clusters.

D A multi-class logistic regression model trained to predict one of the 5 sentiment classes.

💡 Hint

Think about the type of output needed: discrete classes vs continuous values.

Since the task is to classify into 5 distinct sentiment categories, a multi-class classification model like logistic regression with 5 output classes is appropriate. Binary classification only predicts two classes, clustering is unsupervised and may not align with sentiment, and regression predicts continuous values, not discrete classes.

❓ Metrics

intermediate

Evaluating a 5-class sentiment classifier

You trained a 5-class sentiment classifier. Which metric best measures overall accuracy across all classes?

A Accuracy (percentage of correct predictions)

B Precision for the positive class only

C Recall for the negative class only

D Mean Squared Error (MSE)

💡 Hint

Consider a metric that accounts for all classes equally.

Accuracy measures the proportion of correct predictions across all classes, making it suitable for multi-class classification. MSE is for regression, and precision or recall for a single class ignore other classes.

❓ Predict Output

advanced

Output of softmax layer for 5-class sentiment

Given the logits output from a neural network for 5 sentiment classes as [2.0, 1.0, 0.1, 0.5, 3.0], what is the predicted class index after applying softmax and choosing the highest probability?

NLP

import numpy as np
logits = np.array([2.0, 1.0, 0.1, 0.5, 3.0])
exp_logits = np.exp(logits - np.max(logits))
probs = exp_logits / exp_logits.sum()
predicted_class = np.argmax(probs)
print(predicted_class)

A 0

B 4

C 1

D 2

💡 Hint

Softmax picks the class with the highest logit after normalization.

The highest logit is 3.0 at index 4, so softmax probability is highest for class 4, making it the predicted class.

❓ Hyperparameter

advanced

Choosing batch size for training a 5-class sentiment model

You are training a neural network for 5-class sentiment classification on a dataset of 10,000 reviews. Which batch size is likely to give a good balance of training speed and stable gradient updates?

A Batch size of 64

B Batch size of 1 (stochastic gradient descent)

C Batch size of 10,000 (full batch gradient descent)

D Batch size of 1,000,000

💡 Hint

Consider typical batch sizes used in practice for stability and speed.

Batch size 64 is a common choice balancing speed and stable gradients. Batch size 1 is slow and noisy, full batch (10,000) is slow and memory heavy, and 1,000,000 exceeds dataset size and is invalid.

🔧 Debug

expert

Debugging incorrect predictions in 5-class sentiment model

Your 5-class sentiment model always predicts the 'neutral' class regardless of input. Which is the most likely cause?

A The optimizer learning rate is set to a moderate value like 0.001.

B The model uses ReLU activation in hidden layers.

C The model's output layer uses softmax activation but the loss function is mean squared error.

D The training data is perfectly balanced across all 5 classes.

💡 Hint

Think about compatibility between output activation and loss function.

Using softmax output with mean squared error loss can cause poor gradient signals, leading the model to predict the most frequent or default class. Balanced data, ReLU activations, and moderate learning rate are not likely causes of this issue.

Practice

1. What does a fine-grained sentiment analysis with 5 classes typically represent?

easy

A. It translates text into five different languages.

B. It detects whether the text is about five different topics.

C. It summarizes text into five key points.

D. It classifies text into five levels from very negative to very positive feelings.

Solution

1. Step 1: Understand sentiment analysis levels

   Fine-grained sentiment analysis divides feelings into multiple levels, often five, ranging from very negative to very positive.

2. Step 2: Match the description to options

   It classifies text into five levels from very negative to very positive feelings correctly describes this as classifying text by sentiment levels. Other options describe unrelated tasks.

3. Final Answer:

   It classifies text into five levels from very negative to very positive feelings. -> Option D

4. Quick Check:

   Fine-grained sentiment = 5-level sentiment classification [OK]

Hint: Fine-grained means detailed sentiment levels, not topics or languages [OK]

Common Mistakes:

• Confusing sentiment classes with topic categories
• Thinking it translates text instead of analyzing feelings
• Assuming it summarizes text instead of classifying sentiment

2. Which of the following is the correct way to represent sentiment labels for a 5-class fine-grained sentiment model in Python?

easy

A. labels = {1: 'positive', 2: 'neutral', 3: 'negative'}

B. labels = ['very negative', 'negative', 'neutral', 'positive', 'very positive']

C. labels = ['happy', 'sad', 'angry', 'excited']

D. labels = ['positive', 'negative']

Solution

1. Step 1: Identify correct label list for 5-class sentiment

   The 5-class sentiment labels should cover very negative to very positive, exactly five classes.

2. Step 2: Check each option

   labels = ['very negative', 'negative', 'neutral', 'positive', 'very positive'] lists five sentiment levels correctly. Options B, C, and D have wrong counts or unrelated labels.

3. Final Answer:

   labels = ['very negative', 'negative', 'neutral', 'positive', 'very positive'] -> Option B

4. Quick Check:

   5-class sentiment labels = labels = ['very negative', 'negative', 'neutral', 'positive', 'very positive'] [OK]

Hint: Five classes must cover full sentiment range, not fewer or unrelated words [OK]

Common Mistakes:

• Using fewer than five labels
• Using unrelated emotion words
• Confusing label types with numeric codes

3. Given the following Python code snippet for a fine-grained sentiment model prediction, what will be the printed output?

import numpy as np
predictions = np.array([[0.1, 0.2, 0.4, 0.2, 0.1]])
predicted_class = np.argmax(predictions)
print(predicted_class)

medium

A. 2

B. 3

C. 1

D. 0

Solution

1. Step 1: Understand np.argmax on prediction array

   np.argmax returns the index of the highest value in the array. Here, predictions are [0.1, 0.2, 0.4, 0.2, 0.1].

2. Step 2: Find the index of max value

   The max value is 0.4 at index 2 (0-based). So predicted_class = 2.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Max probability index = 2 [OK]

Hint: np.argmax returns index of max value, count from zero [OK]

Common Mistakes:

• Confusing index with value
• Counting indices from 1 instead of 0
• Misreading the prediction array

4. You trained a fine-grained sentiment model with 5 classes but your evaluation shows accuracy stuck at 20%. What is the most likely cause?

medium

A. The model is randomly guessing because the output layer has 5 units but the loss function expects 2 classes.

B. The model is overfitting the training data perfectly.

C. The input text is too long for the model to process.

D. The optimizer learning rate is too high.

Solution

1. Step 1: Analyze low accuracy with 5-class output

   Accuracy near 20% suggests random guessing among 5 classes (1/5 = 20%).

2. Step 2: Check mismatch between output and loss

   If the model output layer has 5 units but the loss function expects 2 classes (binary), the model cannot learn properly, causing random predictions.

3. Final Answer:

   Output layer and loss function class count mismatch causing random guessing. -> Option A

4. Quick Check:

   Mismatch output vs loss classes = random 20% accuracy [OK]

Hint: Check output units match loss classes to avoid random guessing [OK]

Common Mistakes:

• Assuming overfitting causes low accuracy
• Blaming input length without evidence
• Ignoring loss function and output layer mismatch

5. You want to improve a fine-grained sentiment model's performance on imbalanced data where 'neutral' class is very common. Which approach is best?

hard

A. Increase the batch size to speed up training.

B. Remove the 'neutral' class from the dataset to balance classes.

C. Use class weights in the loss function to give more importance to rare classes.

D. Use a simpler model with fewer layers.

Solution

1. Step 1: Understand class imbalance problem

   When one class dominates, the model may ignore rare classes, hurting performance on them.

2. Step 2: Choose method to handle imbalance

   Using class weights in the loss function tells the model to pay more attention to rare classes, improving balanced learning.

3. Final Answer:

   Use class weights in loss to handle imbalanced classes effectively. -> Option C

4. Quick Check:

   Class weights improve learning on rare classes [OK]

Hint: Apply class weights to balance rare vs common classes [OK]

Common Mistakes:

• Removing common classes loses important data
• Changing batch size doesn't fix imbalance
• Simpler models may underfit complex data