Sentiment analysis with scikit-learn in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Model Choice

intermediate

Choosing the best model for sentiment analysis

You want to build a sentiment analysis model using scikit-learn on a dataset of movie reviews labeled positive or negative. Which model is most suitable for this binary text classification task?

A LinearSVC (Support Vector Classifier with linear kernel)

B KMeans clustering

C LinearRegression

D DBSCAN clustering

💡 Hint

Think about models designed for classification, not clustering or regression.

LinearSVC is a linear classifier suitable for binary classification tasks like sentiment analysis. KMeans and DBSCAN are clustering algorithms, not classifiers. LinearRegression is for continuous output, not categories.

❓ Predict Output

intermediate

Output of text vectorization step

What is the shape of the feature matrix X after applying CountVectorizer to 1000 text reviews, if the vocabulary size is 5000?

ML Python

from sklearn.feature_extraction.text import CountVectorizer
texts = ['sample text data'] * 1000
vectorizer = CountVectorizer(max_features=5000)
X = vectorizer.fit_transform(texts)
print(X.shape)

A (5000, 1000)

B (1000, 5000)

C (1000, 1)

D (1, 5000)

💡 Hint

Rows represent samples, columns represent features.

CountVectorizer transforms text into a matrix where each row is a sample and each column is a word feature. With 1000 samples and 5000 features, shape is (1000, 5000).

❓ Hyperparameter

advanced

Choosing the right hyperparameter for LogisticRegression

You train a LogisticRegression model for sentiment analysis. Which hyperparameter controls the strength of regularization to prevent overfitting?

A penalty (Type of regularization)

B max_iter (Maximum iterations for solver)

C C (Inverse of regularization strength)

D solver (Algorithm to use in optimization)

💡 Hint

Look for the parameter that adjusts how much the model avoids complexity.

The 'C' parameter in LogisticRegression is the inverse of regularization strength. Smaller values specify stronger regularization, helping to reduce overfitting.

❓ Metrics

advanced

Interpreting classification report metrics

After training a sentiment classifier, you get these metrics for the positive class: precision=0.8, recall=0.5, f1-score=0.62. What does the low recall indicate?

A The model is overfitting the training data

B The model predicts many false positives

C The model has perfect accuracy

D The model misses many positive reviews (false negatives are high)

💡 Hint

Recall measures how many actual positives are found.

Recall is the fraction of true positives found out of all actual positives. Low recall means many positive samples are missed (false negatives).

🔧 Debug

expert

Debugging unexpected accuracy drop after vectorizer change

You trained a sentiment model with CountVectorizer and got 85% accuracy. After switching to TfidfVectorizer with default settings, accuracy dropped to 70%. What is the most likely cause?

A TfidfVectorizer normalizes word counts, which may reduce the impact of frequent sentiment words

B TfidfVectorizer always produces fewer features than CountVectorizer, causing underfitting

C CountVectorizer applies stemming by default, but TfidfVectorizer does not

D TfidfVectorizer requires labels to be numeric, unlike CountVectorizer

💡 Hint

Think about how TF-IDF changes word importance compared to raw counts.

TfidfVectorizer scales down frequent words, which can reduce the weight of common sentiment words that were important in CountVectorizer, causing accuracy drop.

Practice

1. What is the main goal of sentiment analysis in machine learning?

easy

A. To generate new text based on input

B. To count the number of words in a sentence

C. To translate text from one language to another

D. To find out if text expresses positive or negative feelings

Solution

1. Step 1: Understand sentiment analysis purpose

   Sentiment analysis is about detecting emotions or opinions in text, usually positive or negative feelings.

2. Step 2: Compare options with definition

   Only To find out if text expresses positive or negative feelings matches this goal. Others describe different tasks like counting words or translation.

3. Final Answer:

   To find out if text expresses positive or negative feelings -> Option D

4. Quick Check:

   Sentiment analysis = Detect positive/negative feelings [OK]

Hint: Sentiment analysis = positive or negative text feelings [OK]

Common Mistakes:

• Confusing sentiment analysis with translation
• Thinking it counts words instead of feelings
• Mixing it up with text generation

2. Which scikit-learn class is commonly used to convert text into numbers for sentiment analysis?

easy

A. LinearRegression

B. CountVectorizer

C. KMeans

D. DecisionTreeClassifier

Solution

1. Step 1: Identify text to number conversion tool

   CountVectorizer turns text into a matrix of word counts, a common step before training models.

2. Step 2: Check other options

   LinearRegression predicts numbers, KMeans clusters data, DecisionTreeClassifier classifies but does not convert text directly.

3. Final Answer:

   CountVectorizer -> Option B

4. Quick Check:

   Text to numbers = CountVectorizer [OK]

Hint: CountVectorizer converts text to numbers [OK]

Common Mistakes:

• Choosing regression or clustering classes for text conversion
• Confusing model classes with vectorizers
• Using classifiers directly on raw text

3. What will be the output of this code snippet?

from sklearn.feature_extraction.text import CountVectorizer
texts = ['I love this movie', 'I hate this movie']
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)
print(X.toarray())

medium

A. [[0 1 1 1 1]
[1 1 0 1 1]]

B. [[1 1 1 1 0]
[1 1 1 0 1]]

C. [[1 1 0 1 1]
[1 0 1 1 1]]

D. [[1 0 1 1 1]
[1 1 1 0 1]]

Solution

1. Step 1: Identify vocabulary order

   CountVectorizer sorts words alphabetically: ['hate', 'i', 'love', 'movie', 'this']

2. Step 2: Map texts to counts

   'I love this movie' counts: hate=0, i=1, love=1, movie=1, this=1; 'I hate this movie' counts: hate=1, i=1, love=0, movie=1, this=1

3. Final Answer:

   [[0 1 1 1 1] [1 1 0 1 1]] -> Option A

4. Quick Check:

   Alphabetical words count matrix matches [[0 1 1 1 1] [1 1 0 1 1]] [OK]

Hint: CountVectorizer sorts words alphabetically [OK]

Common Mistakes:

• Assuming original word order instead of alphabetical
• Mixing up counts for 'love' and 'hate'
• Forgetting to convert sparse matrix to array

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

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
texts = ['good', 'bad']
labels = [1, 0]
vectorizer = CountVectorizer()
X = vectorizer.transform(texts)
model = LogisticRegression()
model.fit(X, labels)

medium

A. Labels should be strings, not integers

B. LogisticRegression cannot be used for sentiment analysis

C. Using transform before fit_transform on vectorizer

D. Texts list should be empty

Solution

1. Step 1: Check vectorizer usage

   CountVectorizer must be fit with fit_transform before transform to learn vocabulary.

2. Step 2: Identify error in code

   Code calls transform without fitting first, causing error because vocabulary is unknown.

3. Final Answer:

   Using transform before fit_transform on vectorizer -> Option C

4. Quick Check:

   Vectorizer must fit before transform [OK]

Hint: Always fit vectorizer before transform [OK]

Common Mistakes:

• Calling transform without fitting vectorizer
• Thinking labels must be strings
• Believing LogisticRegression is unsuitable here

5. You trained a sentiment analysis model with scikit-learn and got 90% accuracy on training data but only 60% on new reviews. What should you do to improve real-world performance?

hard

A. Use more diverse training data and tune model parameters

B. Reduce training data size to avoid overfitting

C. Remove stop words from test data only

D. Switch to a regression model instead of classification

Solution

1. Step 1: Understand overfitting signs

   High training accuracy but low test accuracy means model learned training data too well but fails on new data.

2. Step 2: Choose improvement strategy

   Using more diverse data and tuning parameters helps model generalize better. Reducing data or removing stop words only on test is wrong. Regression is not suitable for classification tasks.

3. Final Answer:

   Use more diverse training data and tune model parameters -> Option A

4. Quick Check:

   Overfitting fix = More data + tuning [OK]

Hint: Fix overfitting with more data and tuning [OK]

Common Mistakes:

• Reducing training data size worsens model
• Altering test data only is incorrect
• Confusing classification with regression