[AI from Scratch] Episode 256: Basics of Topic Modeling (LDA)

Recap and Today’s Theme

Hello! In the previous episode, we explored fine-tuning BERT, explaining how to adapt a pre-trained model for specific NLP tasks.

Today, we will delve into Topic Modeling (LDA). Topic modeling is a method for automatically extracting latent topics from documents, and one of the most well-known techniques is Latent Dirichlet Allocation (LDA). This episode explains the basic concept of topic modeling, how LDA works, and how to implement it.

What is Topic Modeling?

1. Basic Concept of Topic Modeling

Topic modeling is a method for extracting latent topics within a document based on the co-occurrence of words. A topic is a collection of words that summarizes the content or theme of a document. With topic modeling, you can:

• Classify large collections of documents and understand their themes.
• Provide relevant information in information retrieval and recommendation systems.
• Analyze the themes of news articles or research papers.

2. Types of Topic Models

There are several types of topic models, but Latent Dirichlet Allocation (LDA) is the most commonly used. LDA models each document as a probabilistic mixture of topics, assuming each topic has a distribution of related words.

How LDA Works

1. Basic Concept of LDA

LDA models the process of document generation as a probabilistic process. It assumes the following steps for generating a document:

1. Determine the topic distribution for each document
   Each document is assumed to contain a mix of topics, and a distribution is set for each document indicating the proportion of topics present.
2. Set the word distribution for each topic
   Each topic has a distribution of words associated with it, which determines the probability of choosing a word based on the topic.
3. Generate words based on the topic distribution
   Randomly select a topic from the document’s topic distribution and then choose a word from the selected topic’s word distribution to generate the document.

2. Dirichlet Distribution

LDA uses a Dirichlet distribution to represent both the topic distribution in documents and the word distribution in topics. The Dirichlet distribution generates probability vectors, controlling the diversity of topics and words. It plays a crucial role in adjusting the richness of the topics and the variety of words within those topics.

3. Mathematical Model of LDA

LDA generates documents through the following steps:

1. For each document ( d ), sample the topic distribution ( \theta_d ) from the Dirichlet distribution.
2. For each topic ( k ), sample the word distribution ( \phi_k ) from the Dirichlet distribution.
3. For each word position ( n ) in the document:

• Select a topic ( z_n ) based on the topic distribution ( \theta_d ).
• Choose a word ( w_n ) based on the word distribution ( \phi_{z_n} ) corresponding to the selected topic.

Implementation of LDA

Here, we implement LDA using Python’s gensim library.

1. Install Required Libraries

First, install the necessary libraries:

pip install gensim
pip install nltk

2. Preparing the Data

To apply topic modeling, preprocess the text data and convert it into a list of words:

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from gensim import corpora
from gensim.models import LdaModel

# Download NLTK data (only needed once)
nltk.download('punkt')
nltk.download('stopwords')

# Sample data
documents = [
    "Machine learning is fascinating.",
    "Artificial intelligence is transforming industries.",
    "Deep learning is a subset of machine learning.",
    "Natural language processing enables computers to understand text."
]

# Preprocess the text
stop_words = set(stopwords.words('english'))

def preprocess(text):
    tokens = word_tokenize(text.lower())
    tokens = [word for word in tokens if word.isalpha() and word not in stop_words]
    return tokens

processed_docs = [preprocess(doc) for doc in documents]

This code tokenizes the text, removes stopwords, and converts everything to lowercase.

3. Building the LDA Model

Create a dictionary and a corpus from the preprocessed text data, then build the LDA model:

# Create a dictionary
dictionary = corpora.Dictionary(processed_docs)

# Create a corpus (a list of term frequencies)
corpus = [dictionary.doc2bow(doc) for doc in processed_docs]

# Train the LDA model
lda_model = LdaModel(corpus, num_topics=2, id2word=dictionary, passes=15)

# Display topics
topics = lda_model.print_topics(num_words=4)
for topic in topics:
    print(topic)

The code above trains an LDA model to extract two topics and displays the words associated with each topic.

Applications of LDA

1. News Article Classification

LDA is effective for extracting topics from news articles. By analyzing the topic distribution of each article, LDA can categorize them based on themes such as politics, sports, or technology.

2. Customer Support Analysis

By applying LDA to customer support inquiries, common topics can be identified, allowing businesses to prioritize and address frequently occurring issues.

3. Academic Paper Analysis

LDA can be used to analyze collections of research papers, identifying the main research themes within a field. This helps in understanding research trends and clustering papers by topic.

Challenges and Improvements in Topic Modeling

1. Determining the Number of Topics

LDA requires specifying the number of topics beforehand, which can be difficult. Techniques exist to optimize the number of topics automatically, but manual adjustment may be necessary based on the data.

2. Word Ambiguity

When the same word has different meanings in different contexts, interpreting topics can become challenging. Incorporating word embeddings like Word2Vec or BERT alongside LDA can improve the accuracy of topic interpretation.

3. Interpretability of Topics

The topics extracted by LDA may be hard to interpret. To make the topics clearer, further analysis of associated words or documents is needed.

Summary

In this episode, we covered the basics of Topic Modeling (LDA), explaining how to extract topics and implement LDA using Python. LDA is a powerful tool for understanding document content and identifying main themes, making it useful in various domains.

Next Episode Preview

Next time, we will explore Named Entity Recognition (NER), learning how to extract proper nouns and other significant entities from text.

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Notes

1. Dirichlet Distribution: A probability distribution used as a prior in LDA for topic and word distributions.
2. Bag-of-Words: A simple text representation based on word frequency.
3. Topic Distribution: The probability distribution of topics in a document.