LDA with scikit-learn in NLP

Introduction

LDA helps find hidden topics in a collection of texts. It groups words that often appear together to understand the main themes.

You want to discover topics in a set of news articles.

You need to organize customer reviews by themes without reading all of them.

You want to summarize large text data by main ideas.

You want to explore themes in social media posts.

You want to reduce text complexity for easier analysis.

Syntax

NLP

from sklearn.decomposition import LatentDirichletAllocation
lda = LatentDirichletAllocation(n_components=number_of_topics, random_state=seed)
lda.fit(document_term_matrix)

n_components sets how many topics you want to find.

document_term_matrix is a matrix where rows are documents and columns are word counts.

Examples

Create an LDA model to find 3 topics and fit it to data X.

NLP

from sklearn.decomposition import LatentDirichletAllocation
lda = LatentDirichletAllocation(n_components=3, random_state=42)
lda.fit(X)

Find 5 topics with up to 10 iterations for better results.

NLP

lda = LatentDirichletAllocation(n_components=5, max_iter=10, random_state=0)
lda.fit(X)

Sample Model

This program finds 2 topics in 5 short texts. It prints the top 3 words for each topic to show what the topic is about.

NLP

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

# Sample documents
texts = [
    'I love reading books about science and technology',
    'The new movie was exciting and full of action',
    'Technology advances help science progress',
    'Action movies are thrilling and fun to watch',
    'Books on science explain complex ideas clearly'
]

# Convert texts to a matrix of token counts
vectorizer = CountVectorizer(stop_words='english')
X = vectorizer.fit_transform(texts)

# Create LDA model to find 2 topics
lda = LatentDirichletAllocation(n_components=2, random_state=0)
lda.fit(X)

# Show top words for each topic
n_top_words = 3
feature_names = vectorizer.get_feature_names_out()
for topic_idx, topic in enumerate(lda.components_):
    top_words = [feature_names[i] for i in topic.argsort()[:-n_top_words - 1:-1]]
    print(f"Topic {topic_idx + 1}: {', '.join(top_words)}")

Important Notes

LDA works best with many documents and a good number of words.

Removing common words (stop words) helps LDA find better topics.

You can tune n_components to get more or fewer topics.

Summary

LDA finds hidden topics by grouping words that appear together.

Use CountVectorizer to turn text into numbers for LDA.

Check top words per topic to understand what each topic means.

Practice

1. What is the main purpose of using LDA (Latent Dirichlet Allocation) in text analysis?

easy

A. To remove stop words from text data

B. To translate text from one language to another

C. To count the number of words in a document

D. To find hidden topics by grouping words that often appear together

Solution

1. Step 1: Understand LDA's goal

   LDA is a method to discover hidden topics in a collection of documents by grouping words that frequently appear together.

2. Step 2: Compare options with LDA's purpose

   Only To find hidden topics by grouping words that often appear together correctly describes this goal. Other options describe different text processing tasks.

3. Final Answer:

   To find hidden topics by grouping words that often appear together -> Option D

4. Quick Check:

   LDA purpose = find hidden topics [OK]

Hint: LDA groups words to reveal hidden themes in text [OK]

Common Mistakes:

• Confusing LDA with translation or word counting
• Thinking LDA removes stop words
• Assuming LDA labels documents directly

2. Which of the following is the correct way to import the LDA model from scikit-learn?

easy

A. from sklearn.decomposition import LatentDirichletAllocation

B. from sklearn.feature_extraction.text import LatentDirichletAllocation

C. from sklearn.decomposition import LDA

D. from sklearn.lda import LatentDirichletAllocation

Solution

1. Step 1: Recall correct import path

   The LDA model in scikit-learn is located in the decomposition module and is named LatentDirichletAllocation.

2. Step 2: Check each option

   from sklearn.decomposition import LatentDirichletAllocation matches the correct import statement. Options B, C, and D use wrong modules or names.

3. Final Answer:

   from sklearn.decomposition import LatentDirichletAllocation -> Option A

4. Quick Check:

   Correct import = sklearn.decomposition.LatentDirichletAllocation [OK]

Hint: LDA is in sklearn.decomposition, not feature_extraction [OK]

Common Mistakes:

• Importing LDA from wrong module
• Using incorrect class name 'LDA'
• Assuming sklearn has a separate lda module

3. Given the following code snippet, what will be the shape of the variable topic_distribution?

from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer

docs = ["apple banana apple", "banana orange banana", "apple orange orange"]
vectorizer = CountVectorizer()
dtm = vectorizer.fit_transform(docs)
lda = LatentDirichletAllocation(n_components=2, random_state=0)
lda.fit(dtm)
topic_distribution = lda.transform(dtm)

medium

A. (2, 3)

B. (3, 2)

C. (3, 3)

D. (2, 2)

Solution

1. Step 1: Understand input and model parameters

   There are 3 documents and the LDA model is set to find 2 topics (n_components=2).

2. Step 2: Determine output shape of lda.transform

   The transform method returns a matrix with rows = number of documents (3) and columns = number of topics (2).

3. Final Answer:

   (3, 2) -> Option B

4. Quick Check:

   Output shape = (documents, topics) = (3, 2) [OK]

Hint: Output shape = (number of docs, number of topics) [OK]

Common Mistakes:

• Confusing number of topics with number of documents
• Swapping rows and columns in output shape
• Assuming transform returns topic-word matrix

4. Identify the error in this code snippet that uses LDA with scikit-learn:

from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer

docs = ["cat dog", "dog mouse", "cat mouse"]
vectorizer = CountVectorizer()
dtm = vectorizer.fit_transform(docs)
lda = LatentDirichletAllocation(n_components=2)
lda.fit_transform(dtm)
print(lda.components_)

medium

A. lda.fit_transform returns a matrix but the code ignores it

B. CountVectorizer should be replaced with TfidfVectorizer

C. lda.components_ attribute does not exist

D. n_components must be equal to number of documents

Solution

1. Step 1: Check usage of fit_transform

   lda.fit_transform returns the topic distribution matrix, but the code does not store or use this output.

2. Step 2: Verify attribute and parameters

   lda.components_ exists and n_components can be any positive integer. CountVectorizer is valid here.

3. Final Answer:

   lda.fit_transform returns a matrix but the code ignores it -> Option A

4. Quick Check:

   fit_transform output must be captured or used [OK]

Hint: Always store fit_transform output to use topic distributions [OK]

Common Mistakes:

• Ignoring fit_transform output
• Thinking components_ attribute is missing
• Believing n_components must match document count

5. You want to find 3 topics from a set of news articles using LDA with scikit-learn. After fitting the model, how do you find the top 3 words that represent each topic?

hard

A. Use CountVectorizer's get_feature_names_out to get top words directly

B. Use lda.transform to get topic distribution, then select words with highest probabilities

C. Use lda.components_ to get word weights, then map top indices to feature names from CountVectorizer

D. Use lda.fit_transform output and pick first 3 words from each document

Solution

1. Step 1: Understand lda.components_ role

   lda.components_ contains the importance (weights) of each word for every topic.

2. Step 2: Map top weights to words

   Use CountVectorizer's get_feature_names_out to get the vocabulary, then select top 3 words per topic by sorting weights.

3. Final Answer:

   Use lda.components_ to get word weights, then map top indices to feature names from CountVectorizer -> Option C

4. Quick Check:

   Top words = components_ + feature names [OK]

Hint: Top words per topic come from components_ and vectorizer vocab [OK]

Common Mistakes:

• Using transform output to find top words
• Assuming vectorizer alone gives topic words
• Picking words directly from documents without weights