What Is Topic Modeling?

Key takeaways

Topic modeling uses machine learning to uncover themes in large text collections, helping professionals analyze unstructured data across many fields.

• According to Glassdoor, the median total pay for a machine learning engineer is $160,000 [1].

• A wide range of data professionals and analysts, such as digital marketers and medical researchers, use topic modeling across many fields.

• You can prepare for a career that uses topic modeling by pursuing data science or computer engineering, both of which can provide a strong foundation in the essential skills.

Learn about the types of topic modeling, who uses it, and its pros and cons. If this interests you, consider enrolling in the IBM Business Intelligence (BI) Analyst Professional Certificate. In around four months, you can gain the in-demand skills and hands-on experience to get job-ready in the field of BI without needing any prior experience. 

What is topic modeling?

Topic modeling is a machine learning technique that identifies groups of similar topics within a collection of texts. This statistical modeling process can help to improve your business operations, make processes more efficient, and create a high-quality customer experience. As data analysis is now a crucial aspect of modern business, topic modeling is another tool you can utilize to assist you in finding success in your sector of the market.

Expanding your knowledge of data analysis techniques can also benefit you. According to the 2026 AI & Data Leadership Executive Benchmark Survey, over 99 percent of companies surveyed say investing in data and artificial intelligence (AI) is a top priority [2].

Types of topic modeling

In essence, you have three common types of topic modeling, which are latent Dirichlet allocation (LDA), probabilistic latent semantic analysis (pLSA), and latent semantic analysis (LSA). These topic modeling methods help analyze a text collection by locating and grouping words based on their frequency of use. By doing so, this natural language processing technique helps to comb through irrelevant words and find the ones that point to valuable information within the collection. Below, you can take a closer look at the three main types of topic modeling:

Latent Dirichlet allocation (LDA)

LDA is one of the more commonly used topic modeling techniques that assumes the words within a document determine what that document’s topic is. It finds the structure within a data set by grouping words into issues based on their relationships. The data is sorted into three levels: topic, word, and document. For example, this technique might suggest ‘biology’ as a topic for a document and then assign words such as “genus” or “carnivore” within that topic.

LDA groups words based on two primary principles. Every document is a mixture of topics, and every topic is a mixture of words. Once words are grouped by topic, the number of times those words and topics occur helps make a document matrix that creates an interconnected network that classifies the data.

Probabilistic latent semantic analysis (pLSA)

By analyzing word co-occurrence, pLSA uses probability to model the connections between words and topics and between topics and documents. The pLSA method can be used for document classification, information retrieval, and content analysis. 

Latent semantic analysis (LSA)

LSA identifies and represents the main ideas within a collection of documents by using the principle that related words tend to cluster within a text. It scans unstructured data to locate previously hidden relationships. The algorithm places this information on both a topic-term and a document-topic matrix. Each cell represents the number of times each word occurs in the text. This helps to reduce the issues caused when a single word with multiple meanings repeats across a text or when various words appear in the text that share the same meaning.

For example, a medical professional might use LSA to sort and group patient demographics to create patient profiles.

What does topic modeling do?

Topic modeling finds underlying topics or themes within a large, unstructured body of text. Because topic modeling is an unsupervised type of machine learning, the algorithm doesn’t require you to provide it with any topic assignments. Instead, it seeks out and creates these topics by grouping words by relevance and recurrence.

It finds common themes and groups those words into clusters. For example, depending on the themes, the topic modeling method might identify certain documents as contracts while labeling others as invoices. Data professionals then use these resulting clusters to visualize, explore, summarize, and analyze the text.

Who uses topic modeling?

A wide range of data professionals and analysts, such as digital marketers and medical researchers, use topic modeling across many fields. Read on to learn how these professionals utilize topic modeling.

Digital marketers

Digital marketers use topic modeling to help gauge the impact of their marketing and content efforts through sentiment analysis. This allows them to adjust messaging based on customer needs.

Medical researchers

Medical researchers use topic modeling for medical document data mining. It can help them group gene sequence data or assist with diagnoses such as breast cancer.

Data analysts in customer service

Data analysts in customer service use topic modeling to comb through mined data at scale to find the average customer experience and response and discover any recurring issues that need addressing. For example, you might use topic modeling to group similar products on your website to help customers find more items they might be interested in. You can also group customer support, ensuring they pass quickly to the right team members.

A machine learning engineer can earn a substantial salary in this field. According to Glassdoor, the median total pay for this position is $160,000 [1]. This figure includes base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation.

Pros and cons of topic modeling

Topic modeling offers benefits such as hidden topic and sentiment identification. However, it also has drawbacks, such as narrow parameters or faulty grouping. Here, you can take a closer look at the pros and cons:

Benefits

Topic modeling visualization makes the mundane task of sorting through heaps of unstructured data much more efficient and effective. It’s easier to identify sentiments or issues that need addressing quickly. It allows you to sort through data at scale and find the underlying themes you might not have discovered otherwise.

Drawbacks

Topic modeling sometimes results in overly specific parameters or does not optimally group the words. It can also make it difficult to understand the difference between words within the same topic due to overlooking the contextual clues. This often results in a professional interacting with the data to extrapolate accurate meanings.

How to start in topic modeling

Topic modeling is a branch of machine learning. To begin working in this field, you’ll want to ensure you have a strong foundation in mathematics. Online courses, videos, or articles help increase your statistics and linear algebra knowledge. Develop a well-rounded understanding of computer science topics. While not all machine learning jobs require a degree, a degree in data science or computer engineering can provide a strong foundation in the essential skills for this field.

Once you have the necessary knowledge, build a portfolio showcasing your expertise and seek out entry-level roles that include topic modeling as an expected task.

Read more: Data Science Major: What You Need to Know Before Declaring

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