Sentiment with context (sarcasm, negation) in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of sentiment prediction with negation handling

Given the following Python code snippet using a simple rule-based sentiment function that handles negation, what is the output when the input sentence is "I do not like this movie"?

NLP

def simple_sentiment(text):
    positive_words = {'like', 'love', 'good', 'great', 'happy'}
    negative_words = {'hate', 'bad', 'terrible', 'sad', 'dislike'}
    words = text.lower().split()
    negation_words = {'not', 'no', 'never'}
    negation = False
    score = 0
    for word in words:
        if word in negation_words:
            negation = True
            continue
        if word in positive_words:
            score += -1 if negation else 1
            negation = False
        elif word in negative_words:
            score += 1 if negation else -1
            negation = False
    if score > 0:
        return 'Positive'
    elif score < 0:
        return 'Negative'
    else:
        return 'Neutral'

print(simple_sentiment("I do not like this movie"))

A Positive

B Negative

C Neutral

D SyntaxError

💡 Hint

Think about how negation flips the sentiment of the word 'like'.

The word 'like' is positive, but it is preceded by 'not', a negation word. The code flips the sentiment score for 'like' to negative, resulting in an overall negative sentiment.

🧠 Conceptual

intermediate

Understanding sarcasm detection challenges

Why is sarcasm particularly difficult for sentiment analysis models to detect?

A Because sarcasm is only present in spoken language, not in text.

B Because sarcasm always uses complex vocabulary that models cannot understand.

C Because sarcasm often uses positive words to express negative feelings, confusing simple word-based models.

D Because sarcasm depends on the length of the sentence, which models ignore.

💡 Hint

Think about how the literal words differ from the intended meaning.

Sarcasm often uses positive words ironically to express negative feelings, which simple models that rely on word sentiment struggle to interpret correctly.

❓ Metrics

advanced

Evaluating sarcasm detection model performance

A sarcasm detection model was tested on 1000 sentences. It correctly identified 80 sarcastic sentences out of 100 sarcastic ones, and correctly identified 890 non-sarcastic sentences out of 900 non-sarcastic ones. What is the model's precision for the sarcastic class?

A 0.89

B 0.50

C 0.44

D 0.80

💡 Hint

Precision = True Positives / (True Positives + False Positives). Calculate false positives first.

True Positives (TP) = 80, False Negatives (FN) = 20 (100-80), True Negatives (TN) = 890, False Positives (FP) = 10 (900-890). Precision = 80 / (80 + 10) = 80 / 90 ≈ 0.89.

🔧 Debug

advanced

Debugging a sarcasm detection model code snippet

What error will the following Python code raise when run? def detect_sarcasm(text): sarcasm_keywords = ['yeah right', 'as if', 'totally'] for phrase in sarcasm_keywords: if phrase in text.lower(): return True return False print(detect_sarcasm(12345))

A AttributeError: 'int' object has no attribute 'lower'

B NameError: name 'text' is not defined

C TypeError: argument of type 'int' is not iterable

D No error, output is False

💡 Hint

Check the method called on the input and the input type.

The code calls text.lower(), but the input is an integer (12345), which does not have a lower() method, causing an AttributeError.

❓ Model Choice

expert

Choosing the best model architecture for sarcasm detection with context

Which model architecture is best suited to detect sarcasm in text by understanding context and subtle language cues?

A A k-nearest neighbors model using TF-IDF vectors

B A simple bag-of-words logistic regression model

C A convolutional neural network (CNN) with fixed word embeddings

D A transformer-based model like BERT fine-tuned on sarcasm-labeled data

💡 Hint

Consider models that capture word order and context deeply.

Transformer models like BERT understand context and subtle language patterns, making them best for sarcasm detection compared to simpler models.

Practice

1. What effect does the word not usually have on sentiment in a sentence?

easy

A. It makes the sentence neutral

B. It always makes the sentence positive

C. It has no effect on sentiment

D. It reverses the sentiment of the following phrase

Solution

1. Step 1: Understand negation in sentiment

   The word not is a negation word that flips the meaning of the phrase it modifies.

2. Step 2: Apply to sentiment analysis

   If a phrase is positive, adding not before it usually makes it negative, and vice versa.

3. Final Answer:

   It reverses the sentiment of the following phrase -> Option D

4. Quick Check:

   Negation flips sentiment = A [OK]

Hint: Negation words flip sentiment meaning quickly [OK]

Common Mistakes:

• Ignoring negation words in sentiment
• Assuming negation always makes positive
• Treating negation as neutral

2. Which of the following is the correct way to handle negation in a simple sentiment analysis code snippet?

easy

A. Ignore negation words and analyze sentiment word by word

B. Flip sentiment polarity of words following negation words

C. Treat negation words as positive sentiment

D. Remove negation words before analysis

Solution

1. Step 1: Identify negation handling in code

   Proper handling means detecting negation words and flipping sentiment of words after them.

2. Step 2: Evaluate options

   Ignoring or removing negation loses meaning; treating negation as positive is wrong.

3. Final Answer:

   Flip sentiment polarity of words following negation words -> Option B

4. Quick Check:

   Flip sentiment after negation = B [OK]

Hint: Flip sentiment after negation words in code [OK]

Common Mistakes:

• Ignoring negation in code
• Removing negation words blindly
• Misclassifying negation as positive

3. Consider this Python code snippet for sentiment scoring with negation handling:

sentence = "I do not like this movie"
words = sentence.split()
sentiment_dict = {"like": 1, "movie": 0}
score = 0
negate = False
for w in words:
    if w == "not":
        negate = True
        continue
    val = sentiment_dict.get(w, 0)
    if negate:
        val = -val
        negate = False
    score += val
print(score)

What is the printed output?

medium

A. -1

B. 0

C. 1

D. 2

Solution

1. Step 1: Trace the loop and negation flag

   Words: ['I', 'do', 'not', 'like', 'this', 'movie'] - 'not' sets negate=True - Next word 'like' has sentiment 1, negated to -1 - 'movie' sentiment 0, no negation - Other words have 0 sentiment

2. Step 2: Calculate total score

   Score = -1 (from 'like') + 0 (from 'movie') + 0 (others) = -1

3. Final Answer:

   -1 -> Option A

4. Quick Check:

   Negation flips 1 to -1 = A [OK]

Hint: Negation flips next word sentiment once [OK]

Common Mistakes:

• Not resetting negate flag after one word
• Ignoring words not in sentiment_dict
• Assuming negation affects all following words

4. The following code tries to handle negation but gives wrong sentiment scores:

sentence = "I am not happy"
words = sentence.split()
sentiment_dict = {"happy": 1}
score = 0
negate = False
for w in words:
    if w == "not":
        negate = True
    val = sentiment_dict.get(w, 0)
    if negate:
        val = -val
    score += val
print(score)

What is the main bug causing incorrect output?

medium

A. Negation flag is never reset after use

B. Sentiment dictionary missing 'not' key

C. Loop skips words after 'not'

D. Score is not initialized to zero

Solution

1. Step 1: Analyze negation flag usage

   Negate is set True on 'not' but never reset to False, so all following words are negated.

2. Step 2: Understand impact on sentiment score

   All words after 'not' get negated, causing wrong total sentiment.

3. Final Answer:

   Negation flag is never reset after use -> Option A

4. Quick Check:

   Negate flag reset missing = C [OK]

Hint: Reset negation flag after negating one word [OK]

Common Mistakes:

• Forgetting to reset negation flag
• Adding keys unnecessarily to sentiment dict
• Assuming loop skips words after negation

5. You want to improve a sentiment model to detect sarcasm, which often reverses sentiment meaning. Which approach is best to handle sarcasm in sentiment analysis?

hard

A. Use a simple bag-of-words model ignoring word order

B. Assign fixed positive sentiment to all sarcastic sentences

C. Add a sarcasm detection module using context and tone features

D. Remove all negation words from the text before analysis

Solution

1. Step 1: Understand sarcasm complexity

   Sarcasm changes sentiment meaning and needs context, tone, or special features beyond simple word counts.

2. Step 2: Evaluate approaches

   Simple bag-of-words or removing negations lose sarcasm cues; fixed positive sentiment is incorrect.

3. Final Answer:

   Add a sarcasm detection module using context and tone features -> Option C

4. Quick Check:

   Sarcasm needs special detection = D [OK]

Hint: Detect sarcasm with context and tone features [OK]

Common Mistakes:

• Using simple models ignoring sarcasm
• Removing negation words blindly
• Assigning fixed sentiment to sarcasm