Part 4. Digital Tools Explained
4.5 Topic Modeling Tool
What is Topic Modeling Tool?
Topic modeling is a type of statistical modeling for discovering the abstract “topics” that occur in a collection of documents. This tool uses an Latent Dirichlet Allocation (LDA) algorithm to classify text in a document to a particular topic. Topic models provide a simple way to analyze large volumes of unlabeled text. A “topic” consists of a cluster of words that frequently occur together.
Overview of Topic Modeling Tool
Teddy Rolad’s offers a fantastic overview of topic modeling in “Topic Modeling: What Humanists Actually Do With It.” In this overview, he points out that “Computers make excellent statisticians and this can be leveraged toward the kind of textual synthesis that initiates higher-order inquiry.” Even though computers cannot interpret meaning, “The computer is well able to recognize unique strings of characters like words and can perform tasks like locating or counting these strings throughout a document.”
Roland cautions readers that “Despite its algorithmic nature, it would be a gross mischaracterization to claim that topic modeling is somehow objective or absent interpretation.” He continues, “I will simply emphasize that human evaluative decisions and textual assumptions are encoded in each step of the process, including text selection and topic scope.”
Scholars might consider using topic modeling as a way to guide close readings and as a technique that examines overarching linguistic patterns in a collection of texts. Topic modeling provides us with methods to organize, understand and summarize large collections of textual information. Ultimately, this technical approach to interpreting texts highlights hidden topical patterns that are present across the collection that might not be seen when performing a traditional read.
This chapter offers an overview of how to use Topic Modeling Tool to explore Toni Morrison’s Sula.
Getting Started with Topic Modeling Tool
What is topic modeling?
Topic modeling is a method of text analysis that looks for clusters of words, called “topics”, in a collection of texts. Topic modeling allows us to examine a body of texts from a distance, find which words tend to cluster together, and examine the general trends among those clusters.
How do I do it?
In this tutorial, we will be using Topic Modeling Tool, a desktop application that runs on Mac and PC.
You will also need a body of texts that you want to examine. These can be chapters from a novel, a collection of journal articles, or any other collection of texts. These texts should be generally related somehow, otherwise the results from the topic modeling will be meaningless!
These texts each need to be saved as separate .txt files in order for the program to work. You can copy and paste your texts into any word processor and save the file as a .txt file. Create a new folder and save all your text files into that folder.
How do I install Topic Modeling Tool?
You