Sentiment analysis pipeline in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main purpose of tokenization in a sentiment analysis pipeline?

In a sentiment analysis pipeline, why do we perform tokenization on the input text?

A To train the model on labeled data

B To convert sentiment scores into numerical values

C To split the text into smaller units like words or subwords for easier processing

D To remove stop words from the text

💡 Hint

Think about how a computer understands text before analyzing sentiment.

Tokenization breaks down text into smaller pieces such as words or subwords, which makes it easier for the model to process and understand the text.

❓ Predict Output

intermediate

What is the output of this preprocessing code snippet?

Given the following Python code for preprocessing text in a sentiment analysis pipeline, what is the output?

NLP

import re
text = "I love this product! It's amazing."
cleaned = re.sub(r'[^a-zA-Z ]', '', text).lower().split()
print(cleaned)

A ['I', 'love', 'this', 'product', 'its', 'amazing']

B ['I', 'love', 'this', 'product', 'It', 's', 'amazing']

C ['i', 'love', 'this', 'product', 'it', 's', 'amazing']

D ['i', 'love', 'this', 'product', 'its', 'amazing']

💡 Hint

Look at how punctuation is removed and text is converted to lowercase.

The regex removes all characters except letters and spaces. Then the text is converted to lowercase and split into words, so "It's" becomes "its" without the apostrophe.

❓ Model Choice

advanced

Which model architecture is best suited for capturing context in sentiment analysis?

You want to build a sentiment analysis model that understands the context of words in a sentence. Which model architecture is most suitable?

A A recurrent neural network (RNN) like LSTM or GRU

B A linear regression model on raw text

C A simple bag-of-words model with logistic regression

D A k-nearest neighbors model using word counts

💡 Hint

Think about models that can remember previous words to understand meaning.

RNNs like LSTM or GRU can capture the order and context of words, which is important for understanding sentiment in sentences.

❓ Metrics

advanced

Which metric is most appropriate to evaluate a sentiment analysis model on imbalanced data?

Your sentiment dataset has many more positive reviews than negative ones. Which evaluation metric should you prioritize?

A F1-score

B Recall

C Accuracy

D Precision

💡 Hint

Consider a metric that balances precision and recall.

F1-score balances precision and recall, making it better for imbalanced datasets where accuracy can be misleading.

🔧 Debug

expert

Why does this sentiment analysis model always predict the same class?

Here is a snippet of a sentiment analysis model training code. The model always predicts the same sentiment class regardless of input. What is the most likely cause?

NLP

import torch
import torch.nn as nn

class SimpleSentimentModel(nn.Module):
    def __init__(self, vocab_size, embed_dim, num_classes):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        self.fc = nn.Linear(embed_dim, num_classes)

    def forward(self, x):
        embedded = self.embedding(x)  # shape: (batch_size, seq_len, embed_dim)
        pooled = embedded.mean(dim=1)  # average over seq_len
        out = self.fc(pooled)
        return out

model = SimpleSentimentModel(vocab_size=1000, embed_dim=50, num_classes=2)

# Training loop omitted for brevity

# After training, model always predicts class 0.

A The model's output layer lacks a softmax or sigmoid activation, so predictions are not probabilities

B The training data is imbalanced and the model learned to predict the majority class

C The embedding layer is not updating because requires_grad is False

D The pooling operation averages embeddings, losing important sequence information

💡 Hint

Consider the effect of unbalanced classes on model predictions.

If the training data is heavily imbalanced, the model may learn to always predict the majority class to minimize loss, resulting in poor generalization.

Practice

1. What is the main purpose of a sentiment analysis pipeline in natural language processing?

easy

A. To automatically detect feelings or opinions in text

B. To translate text from one language to another

C. To count the number of words in a sentence

D. To generate new text based on input

Solution

1. Step 1: Understand the goal of sentiment analysis

   Sentiment analysis is about finding emotions or opinions in text data.

2. Step 2: Identify the pipeline's role

   A sentiment analysis pipeline automates this process to detect feelings like positive or negative.

3. Final Answer:

   To automatically detect feelings or opinions in text -> Option A

4. Quick Check:

   Sentiment analysis = detect feelings [OK]

Hint: Sentiment analysis finds emotions in text fast [OK]

Common Mistakes:

• Confusing sentiment analysis with translation
• Thinking it counts words instead of feelings
• Assuming it generates new text

2. Which of the following is the correct way to create a sentiment analysis pipeline using the Hugging Face Transformers library in Python?

easy

A. pipeline = Pipeline('text-classification')

B. pipeline = create_pipeline('sentiment')

C. pipeline = sentiment_pipeline()

D. pipeline = pipeline('sentiment-analysis')

Solution

1. Step 1: Recall the Hugging Face pipeline syntax

   The correct function is pipeline with the task name as a string.

2. Step 2: Match the exact task name for sentiment analysis

   The task name is 'sentiment-analysis', so pipeline('sentiment-analysis') is correct.

3. Final Answer:

   pipeline = pipeline('sentiment-analysis') -> Option D

4. Quick Check:

   Use pipeline('sentiment-analysis') to create sentiment pipeline [OK]

Hint: Use pipeline('sentiment-analysis') exactly [OK]

Common Mistakes:

• Using wrong function names like create_pipeline
• Missing quotes around task name
• Using incorrect task names like 'sentiment'

3. What will be the output of this Python code using Hugging Face's sentiment analysis pipeline?

from transformers import pipeline
sentiment = pipeline('sentiment-analysis')
result = sentiment('I love sunny days!')
print(result)

medium

A. [{'label': 'NEGATIVE', 'score': 0.99}]

B. [{'label': 'POSITIVE', 'score': 0.99}]

C. SyntaxError

D. []

Solution

1. Step 1: Understand the input text sentiment

   The sentence 'I love sunny days!' expresses a positive feeling.

2. Step 2: Predict output from sentiment pipeline

   The pipeline returns a list with a dictionary containing label 'POSITIVE' and a high confidence score.

3. Final Answer:

   [{'label': 'POSITIVE', 'score': 0.99}] -> Option B

4. Quick Check:

   Positive sentence = POSITIVE label [OK]

Hint: Positive words give POSITIVE label with high score [OK]

Common Mistakes:

• Expecting NEGATIVE label for positive text
• Thinking output is a string, not a list of dict
• Confusing syntax errors with runtime output

4. You wrote this code but get an error: NameError: name 'pipeline' is not defined. What is the likely fix?

sentiment = pipeline('sentiment-analysis')
result = sentiment('I hate rain.')
print(result)

medium

A. Add from transformers import pipeline before using pipeline

B. Change 'sentiment-analysis' to 'sentiment'

C. Replace pipeline with sentiment_pipeline

D. Remove parentheses from pipeline call

Solution

1. Step 1: Identify cause of NameError

   The error means Python does not know what pipeline is because it was not imported.

2. Step 2: Fix by importing pipeline function

   Adding from transformers import pipeline defines pipeline so the code runs correctly.

3. Final Answer:

   Add from transformers import pipeline before using pipeline -> Option A

4. Quick Check:

   Import missing = NameError fixed [OK]

Hint: Always import pipeline before using it [OK]

Common Mistakes:

• Changing task name instead of importing
• Assuming pipeline is built-in without import
• Removing parentheses causing syntax errors

5. You want to analyze customer reviews but some reviews are empty strings or just spaces. How should you modify your sentiment analysis pipeline to handle this before prediction?

hard

A. Replace empty reviews with the word 'neutral' and analyze

B. Pass all reviews directly to the pipeline without changes

C. Filter out empty or whitespace-only reviews before passing to the pipeline

D. Use a different pipeline for empty reviews

Solution

1. Step 1: Understand the problem with empty inputs

   Empty or whitespace-only texts do not contain sentiment and can cause errors or meaningless results.

2. Step 2: Apply filtering before analysis

   Removing or skipping these empty reviews ensures the pipeline only processes valid text, improving accuracy and avoiding errors.

3. Final Answer:

   Filter out empty or whitespace-only reviews before passing to the pipeline -> Option C

4. Quick Check:

   Remove empty inputs before analysis [OK]

Hint: Skip empty reviews to avoid errors [OK]

Common Mistakes:

• Passing empty strings causing errors
• Replacing empty with unrelated words
• Using multiple pipelines unnecessarily