Sentiment analysis with scikit-learn in ML Python - Model Pipeline Trace

Model Pipeline - Sentiment analysis with scikit-learn

This pipeline takes text reviews and teaches a computer to tell if the feeling is positive or negative. It cleans the text, turns words into numbers, trains a simple model, and then checks how well it learned.

Data Flow - 5 Stages

1Raw text data

1000 rows x 1 column→Load text reviews with sentiment labels→1000 rows x 2 columns

['I love this product!', 'This is terrible.'], [1, 0]

↓

2Text cleaning and vectorization

1000 rows x 1 column→Convert text to numbers using CountVectorizer→1000 rows x 5000 columns

[0, 1, 0, ..., 2, 0, 1]

↓

3Train/test split

1000 rows x 5000 columns→Split data into 800 training and 200 testing rows→800 rows x 5000 columns (train), 200 rows x 5000 columns (test)

Train features shape: (800, 5000), Test features shape: (200, 5000)

↓

4Model training

800 rows x 5000 columns→Train Logistic Regression model on training data→Trained model

Model learns weights for each word feature

↓

5Model evaluation

200 rows x 5000 columns→Predict sentiment on test data and calculate accuracy→Accuracy score (scalar)

Accuracy = 0.85

Training Trace - Epoch by Epoch

Loss
0.5 |****
0.4 |****
0.3 |******
0.2 |*******
     1  2  3 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.75	Model starts learning, accuracy is moderate
2	0.3	0.82	Loss decreases, accuracy improves
3	0.25	0.85	Model converges with good accuracy

Prediction Trace - 3 Layers

Layer 1: Text vectorization

Layer 2: Logistic Regression prediction

Layer 3: Threshold decision

Model Quiz - 3 Questions

What does the vectorizer do in this pipeline?

A Splits data into training and testing sets

B Calculates the accuracy of the model

C Turns text into numbers representing word counts

D Predicts sentiment from the text

Key Insight

This visualization shows how text data is turned into numbers so a simple model can learn to tell positive from negative reviews. Watching loss go down and accuracy go up helps us know the model is learning well.

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