How to Add Topic Number to Input Dataframe in Latent Dirichlet Allocation (LDA) Model with R

Understanding LDA Model and Adding Topic Number to Input Dataframe

Introduction

Latent Dirichlet Allocation (LDA) is a topic modeling technique that can be used to analyze large amounts of text data. In this article, we will explore how to add the topic number to the input dataframe in an LDA model.

LDA Basics

What is LDA?

LDA is a probabilistic model for analyzing large documents by representing them as mixtures of topics. Each document is assumed to be a mixture of these topics, and each topic can be represented as a distribution over a set of words.

The LDA model consists of three main components:

Documents: A collection of text data that we want to analyze.
Topics: A set of predefined topics that the documents are assumed to belong to. Each topic is represented by a probability distribution over the vocabulary of the document.
Words: The individual words in the document.

The LDA model estimates the topics and their corresponding word distributions from the document data using Bayesian inference.

Using LDA with R

To use the LDA model, we need to install the required packages. We will be using the stm package for the LDA model implementation.

# Install the stm package
install.packages("stm")

# Load the necessary libraries
library(stm)

Creating an LDA Model

To create an LDA model, we need to provide the document data and specify the number of topics that we want to use. In this example, we will be using 20 topics.

# Create a document-feature matrix
data_corpus_irishbudget2010 <- quanteda::corpus(data = "quanteda.textmodels:data_corpus_irishbudget2010")

# Remove punctuation and numbers from the text data
quant_dfm <- dfm(data_corpus_irishbudget2010, remove_punct = TRUE, remove_numbers = TRUE)

# Create an LDA model with 20 topics
my_lda_fit20 <- stm(quant_dfm, K = 20, verbose = FALSE)

Adding Topic Number to Input Dataframe

Once we have created the LDA model, we can add the topic number to the input dataframe by binding the document metadata with the estimated word distribution.

# Convert the document-feature matrix to a stem object
quant_stm <- convert(quant_dfm, to = "stm")

# Estimate the word distribution for each document
result <- cbind(quant_stm$meta, maxtopic = apply(my_lda_fit20$theta, 1, which.max))

Final Result

The final result is a dataframe that contains the original metadata and the topic number for each document.

# Print the result
print(result)

This concludes our example of how to add the topic number to the input dataframe in an LDA model.

Last modified on 2023-11-24