Aspect-based sentiment analysis in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the aspect-based sentiment analysis model.

NLP

from transformers import pipeline

nlp = pipeline([1], model='nlptown/bert-base-multilingual-uncased-sentiment')

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

A'aspect-based-sentiment-analysis'

B'sentiment-analysis'

C'text-classification'

D'ner'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sentiment-analysis' instead of 'aspect-based-sentiment-analysis'.

Using 'ner' which is for named entity recognition.

The pipeline type for aspect-based sentiment analysis is 'aspect-based-sentiment-analysis'.

2fill in blank

medium

Complete the code to extract aspect terms from the input text.

NLP

text = "The battery life of this phone is amazing but the screen is dull."
results = nlp(text)
aspects = [item['[1]'] for item in results]

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

Alabel

Baspect

Cscore

Dsentiment

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'label' which usually indicates sentiment polarity.

Using 'score' which is the confidence value.

The key 'aspect' holds the aspect term extracted from the text.

3fill in blank

hard

Fix the error in the code to correctly get the sentiment label for each aspect.

NLP

for item in results:
    print(item['[1]'])

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

Alabel

Bsentiment

Caspect

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'aspect' which is the feature, not the sentiment.

Using 'sentiment' which is not a key in the result dictionary.

The sentiment label is stored under the key 'label' in each result item.

4fill in blank

hard

Fill both blanks to create a dictionary mapping each aspect to its sentiment label.

NLP

aspect_sentiments = {item[[1]]: item[[2]] for item in results}

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

A'aspect'

B'label'

C'sentiment'

D'score'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sentiment' which is not a valid key.

Using 'score' which is a confidence value, not a label.

The dictionary keys are aspect terms ('aspect') and values are sentiment labels ('label').

5fill in blank

hard

Fill all three blanks to filter aspects with positive sentiment and create a list of their terms.

NLP

positive_aspects = [item[[1]] for item in results if item[[2]] == [3]]

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

A'aspect'

B'label'

C'positive'

D'score'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'score' instead of 'label' for sentiment filtering.

Using 'sentiment' which is not a valid key.

We select the 'aspect' terms where the 'label' equals 'positive'.

Practice

1. What is the main goal of aspect-based sentiment analysis?

easy

A. To translate text from one language to another

B. To count the number of words in a sentence

C. To find feelings about specific parts or features in text

D. To generate new text based on input

Solution

1. Step 1: Understand the concept of aspect-based sentiment analysis

   It focuses on identifying opinions about specific parts or features, not the whole text.

2. Step 2: Compare options with this concept

   Only To find feelings about specific parts or features in text matches this goal; others describe unrelated tasks.

3. Final Answer:

   To find feelings about specific parts or features in text -> Option C

4. Quick Check:

   Aspect-based sentiment = specific parts sentiment [OK]

Hint: Focus on 'specific parts' in the question to find the goal [OK]

Common Mistakes:

• Confusing overall sentiment with aspect-level sentiment
• Thinking it translates or generates text
• Mixing sentiment analysis with word counting

2. Which Python library is commonly used for simple aspect-based sentiment analysis?

easy

A. Matplotlib

B. NumPy

C. Flask

D. TextBlob

Solution

1. Step 1: Identify libraries related to text sentiment

   TextBlob is a simple library for sentiment analysis and text processing.

2. Step 2: Eliminate unrelated libraries

   NumPy is for numbers, Matplotlib for plotting, Flask for web apps, so they don't fit.

3. Final Answer:

   TextBlob -> Option D

4. Quick Check:

   TextBlob = simple sentiment tool [OK]

Hint: Pick the library known for text sentiment, not numbers or web [OK]

Common Mistakes:

• Choosing NumPy or Matplotlib which are not for sentiment
• Confusing Flask as a sentiment tool
• Not knowing TextBlob's purpose

3. Given this Python code snippet for aspect sentiment, what is the output?

from textblob import TextBlob
text = "The battery life is great but the screen is dull."
aspects = ['battery life', 'screen']
results = {}
for aspect in aspects:
    blob = TextBlob(text)
    if aspect in text:
        sentiment = blob.sentiment.polarity
        results[aspect] = 'positive' if sentiment > 0 else 'negative'
print(results)

medium

A. {'battery life': 'positive', 'screen': 'positive'}

B. {'battery life': 'positive', 'screen': 'negative'}

C. {'battery life': 'negative', 'screen': 'negative'}

D. SyntaxError

Solution

1. Step 1: Understand the sentiment polarity calculation

   TextBlob calculates overall sentiment polarity of the whole text, which is positive because 'battery life is great' outweighs 'screen is dull'.

2. Step 2: Check how results are assigned

   For each aspect found in text, sentiment polarity is checked once (overall), so both aspects get 'positive'.

3. Final Answer:

   {'battery life': 'positive', 'screen': 'positive'} -> Option A

4. Quick Check:

   Overall sentiment positive -> both aspects positive [OK]

Hint: TextBlob sentiment is overall, so all aspects get same polarity [OK]

Common Mistakes:

• Assuming aspect-specific sentiment without extra processing
• Expecting different sentiment for each aspect
• Thinking code has syntax errors

4. Identify the error in this aspect-based sentiment analysis code snippet:

from textblob import TextBlob
text = "The food was tasty but the service was slow."
aspects = ['food', 'service']
results = {}
for aspect in aspects:
    blob = TextBlob(aspect)
    sentiment = blob.sentiment.polarity
    results[aspect] = 'positive' if sentiment > 0 else 'negative'
print(results)

medium

A. The aspects list is empty

B. TextBlob is applied to aspect text, not the full sentence

C. The results dictionary is not initialized

D. The print statement is missing parentheses

Solution

1. Step 1: Check what text is analyzed by TextBlob

   TextBlob is called on the aspect word only, not the full sentence, so sentiment is meaningless.

2. Step 2: Identify correct usage

   TextBlob should analyze the full sentence or relevant sentence part, not just the aspect word.

3. Final Answer:

   TextBlob is applied to aspect text, not the full sentence -> Option B

4. Quick Check:

   TextBlob needs full text, not just aspect [OK]

Hint: Check what text TextBlob analyzes--aspect or full sentence? [OK]

Common Mistakes:

• Thinking aspects list is empty
• Ignoring that results dict is initialized
• Assuming print syntax error in Python 3

5. You want to improve aspect-based sentiment analysis by focusing on sentences mentioning each aspect separately. Which approach is best?

hard

A. Split text into sentences, analyze sentiment only for sentences containing the aspect

B. Analyze the entire text sentiment once and assign it to all aspects

C. Ignore sentences and analyze only aspect words with TextBlob

D. Use random sentiment values for each aspect

Solution

1. Step 1: Understand the problem with overall sentiment

   Overall sentiment mixes all opinions, so it can't tell which aspect is positive or negative.

2. Step 2: Use sentence-level analysis for precision

   Splitting text into sentences and analyzing only those mentioning the aspect gives accurate aspect sentiment.

3. Final Answer:

   Split text into sentences, analyze sentiment only for sentences containing the aspect -> Option A

4. Quick Check:

   Sentence-level sentiment = better aspect accuracy [OK]

Hint: Analyze sentences mentioning aspect, not whole text [OK]

Common Mistakes:

• Using overall sentiment for all aspects
• Analyzing only aspect words without context
• Assigning random sentiment values