Why topic modeling discovers themes in NLP

Introduction

Topic modeling helps find hidden themes in lots of text. It groups words that often appear together to show what the text is about.

You have many articles and want to know the main subjects without reading all.

You want to organize customer reviews by common topics.

You need to summarize large documents by their main ideas.

You want to explore themes in social media posts quickly.

You want to help a search engine understand what topics are in documents.

Syntax

NLP

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

# Prepare text data
texts = ["text one", "text two", ...]

# Convert texts to word counts
vectorizer = CountVectorizer()
word_counts = vectorizer.fit_transform(texts)

# Create LDA model
number_of_topics = 5  # example number
lda = LatentDirichletAllocation(n_components=number_of_topics)

# Fit model to data
lda.fit(word_counts)

# Get topics
topics = lda.components_

Latent Dirichlet Allocation (LDA) is a common method for topic modeling.

CountVectorizer turns text into numbers by counting words.

Examples

This finds 3 topics in the text data.

NLP

lda = LatentDirichletAllocation(n_components=3)
lda.fit(word_counts)

This removes common English words like 'the' to focus on meaningful words.

NLP

vectorizer = CountVectorizer(stop_words='english')
word_counts = vectorizer.fit_transform(texts)

Sample Model

This program finds 2 main topics in 5 short texts. It shows the top 5 words for each topic to understand the themes.

NLP

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

texts = [
    "I love reading books about science and technology.",
    "The new movie about space exploration was amazing.",
    "Technology and science are changing the world.",
    "Movies and books can teach us about history and culture.",
    "Space missions require advanced technology and science."
]

vectorizer = CountVectorizer(stop_words='english')
word_counts = vectorizer.fit_transform(texts)

lda = LatentDirichletAllocation(n_components=2, random_state=42)
lda.fit(word_counts)

feature_names = vectorizer.get_feature_names_out()

for i, topic in enumerate(lda.components_):
    top_words = [feature_names[index] for index in topic.argsort()[-5:][::-1]]
    print(f"Topic {i+1}: {', '.join(top_words)}")

Important Notes

Topic modeling does not label topics; you interpret the word groups to find themes.

Choosing the number of topics (n_components) affects results; try different values.

Removing common words (stop words) helps focus on important words.

Summary

Topic modeling groups words that appear together to find themes in text.

LDA is a popular method that uses word counts to discover topics.

Interpreting the top words in each topic helps understand the main ideas.