Topic Modeling in Natural Language Processing
Topic modeling is an unsupervised learning technique used to automatically
discover hidden themes or topics within a large collection of text documents.
Unlike text classification, topic modeling does not require labeled data.
It is especially useful when working with massive text datasets such as news
articles, research papers, blogs, or customer reviews where manual labeling
is not feasible.
⭐ What is Topic Modeling?
Topic modeling identifies groups of words that frequently appear together
and represents them as topics. Each document is associated with one or more
topics, and each topic is represented by a set of keywords.
📌 Why Topic Modeling is Important
- Automatically organizes large text collections
- Finds hidden patterns in documents
- Helps in content discovery and summarization
- Works without labeled data
📌 Popular Topic Modeling Techniques
- Latent Dirichlet Allocation (LDA)
- Non-Negative Matrix Factorization (NMF)
- Probabilistic Latent Semantic Analysis (PLSA)
📌 Latent Dirichlet Allocation (LDA)
Latent Dirichlet Allocation (LDA) is the most widely used topic modeling algorithm.
It assumes that each document is a mixture of topics and each topic is a mixture
of words.
How LDA Works:
- Documents are represented as topic distributions
- Topics are represented as word distributions
- Uses probability to assign topics to documents
📌 Example: Topic Modeling Using LDA
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
documents = [
"AI and machine learning are transforming technology",
"Politics and government policies affect the economy",
"Sports events bring people together",
"Technology companies invest in AI research"
]
vectorizer = CountVectorizer(stop_words='english')
X = vectorizer.fit_transform(documents)
lda = LatentDirichletAllocation(n_components=2, random_state=42)
lda.fit(X)
Extracting Topics:
words = vectorizer.get_feature_names_out()
for idx, topic in enumerate(lda.components_):
print(f"Topic {idx}:")
print([words[i] for i in topic.argsort()[-5:]])
📌 Choosing the Number of Topics
- Based on domain knowledge
- Using coherence score
- Experimentation and evaluation
📌 Real-Life Applications
- News article clustering
- Research paper categorization
- Customer feedback analysis
- Trend discovery in social media
📌 Project Title
Automatic Topic Discovery and Document Clustering System
📌 Project Description
In this project, you will build a topic modeling system using LDA to automatically
discover major themes from a large collection of text documents. The system can
be used for organizing blogs, analyzing feedback, or summarizing research content.
📌 Summary
Topic modeling allows machines to explore and understand large text datasets
without supervision. By using LDA and related techniques, meaningful topics
can be extracted, enabling better content organization and insight discovery.
This chapter prepares you for modern transformer-based NLP models.
