Sentiment analysis with scikit-learn in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is sentiment analysis?

Sentiment analysis is the process of identifying and categorizing opinions or feelings expressed in text, usually as positive, negative, or neutral.

beginner

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

The CountVectorizer or TfidfVectorizer classes convert text into numerical features that machine learning models can use.

beginner

Why do we split data into training and testing sets in sentiment analysis?

Splitting data helps us train the model on one part and test how well it works on new, unseen data to check if it learned correctly.

beginner

What metric is commonly used to measure the accuracy of a sentiment analysis model?

Accuracy measures the percentage of correct predictions the model makes on the test data.

beginner

Name a simple machine learning model used for sentiment analysis with scikit-learn.

The LogisticRegression model is often used because it works well for text classification tasks like sentiment analysis.

What does CountVectorizer do in sentiment analysis?

A Converts text into numbers by counting word occurrences

B Predicts sentiment directly from text

C Splits data into training and testing sets

D Measures model accuracy

CountVectorizer turns text into a matrix of word counts, which helps the model understand the text.

Why do we use a test set in sentiment analysis?

A To train the model

B To check how well the model predicts new data

C To convert text to numbers

D To improve the model's speed

The test set checks if the model learned well by testing it on data it hasn't seen before.

Which metric tells us the percentage of correct sentiment predictions?

A Accuracy

B Recall

C Precision

D Loss

Accuracy shows how many predictions the model got right out of all predictions.

Which scikit-learn model is simple and effective for sentiment analysis?

A PCA

B KMeans

C LogisticRegression

D RandomForestRegressor

LogisticRegression is a popular choice for classifying text into sentiments.

What is the first step before training a sentiment analysis model?

A Predict sentiment

B Convert text to numbers

C Evaluate accuracy

D Split data into train and test

Splitting data into training and testing sets is the first step to ensure proper evaluation of the model.

Explain the main steps to build a sentiment analysis model using scikit-learn.

Think about data preparation, model training, and evaluation.

Describe why converting text into numbers is important for sentiment analysis.

Consider how computers understand text.

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