Sentiment analysis pipeline in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the necessary library for text vectorization.

NLP

from sklearn.feature_extraction.text import [1]

Drag options to blanks, or click blank then click option'

ATfidfVectorizer

BLabelEncoder

CCountVectorizer

Dtrain_test_split

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing CountVectorizer which counts word occurrences but does not weight them.

Using LabelEncoder which is for labels, not text features.

The TfidfVectorizer converts text data into numerical features based on term frequency-inverse document frequency, which is commonly used in sentiment analysis.

2fill in blank

medium

Complete the code to split the dataset into training and testing sets.

NLP

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=[1], random_state=42)

Drag options to blanks, or click blank then click option'

A0.1

B0.2

C0.5

D1.0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1.0 which means all data is test set.

Using 0.5 which splits data evenly but is less common.

A common split for testing is 20% of the data, so test_size=0.2 is used here.

3fill in blank

hard

Fix the error in the model training code by completing the missing method.

NLP

model = LogisticRegression()
model.[1](X_train, y_train)

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Apredict

Bscore

Ctransform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict before training causes errors.

Using transform is for feature transformers, not models.

The fit method trains the model on the training data.

4fill in blank

hard

Complete the code to create a dictionary of predictions and calculate accuracy.

NLP

predictions = model.[1](X_test)
accuracy = accuracy_score(y_test, predictions,)

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Apredict

B)

C,

Dpredict_proba

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict_proba which returns probabilities, not labels.

Missing the comma between arguments in accuracy_score.

Use predict to get predicted labels, then separate arguments with a comma in accuracy_score.

5fill in blank

hard

Fill all three blanks to build a simple sentiment analysis pipeline.

NLP

from sklearn.pipeline import Pipeline
pipeline = Pipeline([
    ('vectorizer', [1]()),
    ('classifier', [2]())
])
pipeline.[3](X_train, y_train)

Drag options to blanks, or click blank then click option'

ATfidfVectorizer

BLogisticRegression

Cfit

DCountVectorizer

Attempts:

3 left

💡 Hint

Common Mistakes

Using CountVectorizer instead of TfidfVectorizer which weights terms.

Calling predict instead of fit to train the pipeline.

The pipeline uses TfidfVectorizer to convert text, LogisticRegression as the model, and fit to train.

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