Lexicon-based approaches (VADER) in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the VADER sentiment analyzer from NLTK.

NLP

from nltk.sentiment.vader import [1]

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

ASentimentIntensityAnalyzer

BSentimentAnalyzer

CVaderAnalyzer

DSentimentScore

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect class names like SentimentAnalyzer or VaderAnalyzer.

The correct class to import for VADER sentiment analysis is SentimentIntensityAnalyzer.

2fill in blank

medium

Complete the code to create an instance of the VADER sentiment analyzer.

NLP

analyzer = [1]()

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

AVaderSentiment

BSentimentIntensityAnalyzer

CSentimentAnalyzer

DVaderAnalyzer

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to instantiate classes that don't exist like VaderSentiment.

You create an instance by calling SentimentIntensityAnalyzer().

3fill in blank

hard

Fix the error in the code to get the sentiment scores for the text.

NLP

scores = analyzer.[1]('I love learning AI!')

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

Apolarity_scores

Bscore

Csentiment_score

Dget_scores

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent methods like score or get_scores.

The method to get sentiment scores is polarity_scores.

4fill in blank

hard

Complete the code to extract the compound score from the sentiment scores dictionary.

NLP

compound_score = scores['[1]']

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

A[

B]

Ccompound

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using parentheses instead of brackets or wrong keys like 'score'.

To get the compound score, use scores['compound'].

5fill in blank

hard

Complete the code to create a dictionary comprehension that maps sentences to their compound sentiment scores.

NLP

sentence_scores = {sentence: analyzer.[1](sentence)['compound'] for sentence in sentences}

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

A:

Bpolarity_scores

C[

D]

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong separators or missing brackets for dictionary access.

The dictionary comprehension uses sentence: analyzer.polarity_scores(sentence)['compound'] for each sentence.

Practice

1. What is the main purpose of the VADER lexicon-based approach in NLP?

easy

A. To generate new text based on input prompts

B. To translate text from one language to another

C. To detect named entities like people and places

D. To analyze the sentiment of text using a list of words with scores

Solution

1. Step 1: Understand VADER's function

   VADER uses a predefined list of words with sentiment scores to analyze feelings in text.

2. Step 2: Compare with other NLP tasks

   Translation, text generation, and entity detection are different tasks not done by VADER.

3. Final Answer:

   To analyze the sentiment of text using a list of words with scores -> Option D

4. Quick Check:

   VADER = sentiment analysis [OK]

Hint: VADER scores words to find text feelings fast [OK]

Common Mistakes:

• Confusing sentiment analysis with translation
• Thinking VADER generates text
• Mixing up sentiment with entity recognition

2. Which of the following is the correct way to import and initialize VADER's SentimentIntensityAnalyzer in Python?

easy

A. from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()

B. import vader
analyzer = vader.SentimentIntensityAnalyzer()

C. from vaderSentiment import SentimentAnalyzer
analyzer = SentimentAnalyzer()

D. import SentimentIntensityAnalyzer from vaderSentiment
analyzer = SentimentIntensityAnalyzer()

Solution

1. Step 1: Recall correct import syntax

   The correct import is from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer.

2. Step 2: Check initialization

   Creating an instance is done by calling SentimentIntensityAnalyzer() with parentheses.

3. Final Answer:

   from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\nanalyzer = SentimentIntensityAnalyzer() -> Option A

4. Quick Check:

   Correct import and init = from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer analyzer = SentimentIntensityAnalyzer() [OK]

Hint: Use full module path and parentheses to init [OK]

Common Mistakes:

• Using wrong module name or missing submodule
• Forgetting parentheses when creating analyzer
• Incorrect import syntax causing errors

3. Given the code below, what will be the output of print(scores)?

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
scores = analyzer.polarity_scores('I love sunny days but hate the rain.')

medium

A. {'neg': 0.5, 'neu': 0.5, 'pos': 0.0, 'compound': -0.5}

B. {'neg': 0.25, 'neu': 0.5, 'pos': 0.25, 'compound': 0.34}

C. {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}

D. SyntaxError due to wrong method call

Solution

1. Step 1: Analyze the sentence sentiment

   The sentence has positive words ('love', 'sunny') and negative word ('hate'). VADER balances these.

2. Step 2: Understand VADER output format

   VADER returns a dict with 'neg', 'neu', 'pos', and 'compound' scores summing to 1 for neg, neu, pos.

3. Final Answer:

   {'neg': 0.25, 'neu': 0.5, 'pos': 0.25, 'compound': 0.34} -> Option B

4. Quick Check:

   Mixed sentiment sentence = balanced scores [OK]

Hint: Positive and negative words balance scores near 0.3-0.4 [OK]

Common Mistakes:

• Expecting all positive or all negative scores
• Confusing compound score with individual scores
• Thinking method call causes syntax error

4. Identify the error in the following code snippet using VADER and how to fix it:

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer
scores = analyzer.polarity_scores('This is great!')

medium

A. String input must be a list; fix by wrapping text in []

B. Wrong import statement; fix by changing module name

C. Missing parentheses when creating analyzer instance; fix by adding ()

D. Method polarity_scores does not exist; fix by using analyze_scores

Solution

1. Step 1: Check how analyzer is created

   Analyzer is assigned the class itself, missing parentheses to create an instance.

2. Step 2: Fix by adding parentheses

   Change to SentimentIntensityAnalyzer() to create an object before calling polarity_scores.

3. Final Answer:

   Missing parentheses when creating analyzer instance; fix by adding () -> Option C

4. Quick Check:

   Instance creation needs () [OK]

Hint: Remember () to create object instances [OK]

Common Mistakes:

• Calling method on class, not instance
• Incorrect import causing attribute errors
• Passing wrong input types to polarity_scores

5. You want to analyze a batch of short tweets using VADER and classify each as positive if the compound score is above 0.05, negative if below -0.05, and neutral otherwise. Which code snippet correctly implements this?

hard

A. from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
tweets = ['Good job!', 'I hate this', 'It is okay.']
results = []
for tweet in tweets:
score = analyzer.polarity_scores(tweet)['compound']
if score > 0.05:
results.append('positive')
elif score < -0.05:
results.append('negative')
else:
results.append('neutral')
print(results)

B. from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
tweets = ['Good job!', 'I hate this', 'It is okay.']
results = []
for tweet in tweets:
score = analyzer.polarity_scores(tweet)['compound']
if score >= 0.05:
results.append('positive')
elif score <= -0.05:
results.append('negative')
else:
results.append('neutral')
print(results)

C. from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
tweets = ['Good job!', 'I hate this', 'It is okay.']
results = []
for tweet in tweets:
score = analyzer.polarity_scores(tweet)['compound']
if score > 0:
results.append('positive')
elif score < 0:
results.append('negative')
else:
results.append('neutral')
print(results)

D. from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
tweets = ['Good job!', 'I hate this', 'It is okay.']
results = []
for tweet in tweets:
score = analyzer.polarity_scores(tweet)['compound']
if score > 0.1:
results.append('positive')
elif score < -0.1:
results.append('negative')
else:
results.append('neutral')
print(results)

Solution

1. Step 1: Understand classification thresholds

   The problem states positive if compound > 0.05, negative if < -0.05, neutral otherwise.

2. Step 2: Check code conditions

   from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer analyzer = SentimentIntensityAnalyzer() tweets = ['Good job!', 'I hate this', 'It is okay.'] results = [] for tweet in tweets: score = analyzer.polarity_scores(tweet)['compound'] if score > 0.05: results.append('positive') elif score < -0.05: results.append('negative') else: results.append('neutral') print(results) uses > 0.05 and < -0.05 exactly, matching the problem statement.

3. Final Answer:

   Option A code correctly implements the classification thresholds -> Option A

4. Quick Check:

   Thresholds match problem = from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer analyzer = SentimentIntensityAnalyzer() tweets = ['Good job!', 'I hate this', 'It is okay.'] results = [] for tweet in tweets: score = analyzer.polarity_scores(tweet)['compound'] if score > 0.05: results.append('positive') elif score < -0.05: results.append('negative') else: results.append('neutral') print(results) [OK]

Hint: Match exact threshold signs for correct classification [OK]

Common Mistakes:

• Using >= or <= instead of > and <
• Changing threshold values incorrectly
• Misclassifying neutral scores