Aspect-based sentiment analysis in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Aspect-based Sentiment Analysis

Which statement best describes the main goal of aspect-based sentiment analysis?

A To classify the overall sentiment of a whole document without focusing on specific parts.

B To identify specific aspects or features in text and determine the sentiment expressed about each aspect.

C To translate text from one language to another while preserving sentiment.

D To generate new text that mimics the sentiment style of the input.

💡 Hint

Think about how aspect-based sentiment analysis differs from general sentiment analysis.

Aspect-based sentiment analysis focuses on extracting sentiments related to specific parts or features (aspects) of a product or service, rather than the overall sentiment.

❓ Predict Output

intermediate

Output of Aspect Extraction Code

What is the output of the following Python code that extracts aspects from a review?

NLP

import spacy
nlp = spacy.load('en_core_web_sm')
text = "The battery life of this phone is amazing but the screen is dull."
doc = nlp(text)
aspects = [chunk.text for chunk in doc.noun_chunks if 'battery' in chunk.text or 'screen' in chunk.text]
print(aspects)

A ['The battery', 'the screen']

B ['battery life', 'screen']

C ['battery', 'screen']

D ['The battery life', 'the screen']

💡 Hint

Look at how noun chunks are extracted and what text they include.

The noun_chunks include determiners like 'The' and 'the', so the output includes 'The battery life' and 'the screen'.

❓ Model Choice

advanced

Choosing a Model for Aspect-based Sentiment Analysis

You want to build an aspect-based sentiment analysis system that can handle multiple aspects per sentence and understand context well. Which model architecture is best suited?

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

B A basic feedforward neural network using only word counts as input.

C A transformer-based model like BERT fine-tuned for aspect-based sentiment classification.

D A rule-based keyword matching system without machine learning.

💡 Hint

Consider models that understand context and multiple aspects in a sentence.

Transformer models like BERT capture context and can be fine-tuned to classify sentiment for multiple aspects in a sentence effectively.

❓ Metrics

advanced

Evaluating Aspect-based Sentiment Analysis Performance

Which metric is most appropriate to evaluate an aspect-based sentiment analysis model that predicts sentiment polarity for each aspect in a review?

A Accuracy computed over all aspect-sentiment pairs.

B BLEU score measuring text generation quality.

C Mean Squared Error between predicted and true sentiment scores.

D Perplexity measuring language model uncertainty.

💡 Hint

Think about how to measure correctness of predicted sentiment labels per aspect.

Accuracy over aspect-sentiment pairs measures how often the model correctly predicts the sentiment for each aspect, which is suitable for classification tasks.

🔧 Debug

expert

Debugging Incorrect Sentiment Predictions

You trained an aspect-based sentiment analysis model but it often predicts neutral sentiment for aspects that clearly have positive or negative sentiment in the text. Which is the most likely cause?

A The training data has many neutral labels causing the model to be biased towards neutral predictions.

B The tokenizer used removes all adjectives from the input text.

C The model architecture is too complex and overfits the training data.

D The optimizer learning rate is too high causing the model to converge quickly.

💡 Hint

Consider how label distribution affects model predictions.

If the training data is imbalanced with many neutral labels, the model may learn to predict neutral too often, ignoring positive or negative signals.