Sentiment analysis with scikit-learn in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the class used to convert text data into numbers.

ML Python

from sklearn.feature_extraction.text import [1]

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

ALinearRegression

BCountVectorizer

CKMeans

DDecisionTreeClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a regression or clustering class instead of a text vectorizer.

Confusing the vectorizer with a classifier.

The CountVectorizer class converts text into a matrix of token counts, which is essential for text-based machine learning tasks like sentiment analysis.

2fill in blank

medium

Complete the code to create a logistic regression model for sentiment classification.

ML Python

from sklearn.linear_model import [1]

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

ALinearRegression

BSVC

CLogisticRegression

DRandomForestClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Using linear regression which is for continuous values, not classification.

Choosing clustering or tree-based models by mistake.

LogisticRegression is commonly used for binary classification tasks like sentiment analysis.

3fill in blank

hard

Fix the error in the code to fit the vectorizer on the training text data.

ML Python

vectorizer = CountVectorizer()
vectorizer.[1](train_texts)

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

Afit

Btransform

Cpredict

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform before fitting causes errors.

Using predict on the vectorizer which is not a model.

The fit method learns the vocabulary from the training texts. Using fit_transform would both fit and transform, but here only fitting is needed before separate transformation.

4fill in blank

hard

Fill both blanks to transform training texts and train the logistic regression model.

ML Python

X_train = vectorizer.[1](train_texts)
model.[2](X_train, train_labels)

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

Afit_transform

Bfit

Ctransform

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform without fitting vectorizer first.

Trying to predict before training the model.

First, fit_transform converts texts to numbers and learns vocabulary. Then, fit trains the model on these features and labels.

5fill in blank

hard

Fill all three blanks to predict sentiment on test data and calculate accuracy.

ML Python

X_test = vectorizer.[1](test_texts)
predictions = model.[2](X_test)
accuracy = accuracy_score(test_labels, [3])

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

Atransform

Bpredict

Cpredictions

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit on test data which leaks training info.

Passing wrong variable to accuracy function.

We transform test texts with transform, predict labels with predict, and then compare predictions to true labels to get accuracy.

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