What Is Topic Modeling?

Data is a cornerstone of business analytics. Once collected, professionals analyze structured and unstructured data to uncover insights and inform strategic decisions that drive growth.

One method for analyzing unstructured data is topic modeling. Read on to learn what topic modeling is, the benefits of topic modeling visualization, and what types of topic modeling exist, such as NLP topic modeling.

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 data analysis tool you can utilize to assist you in finding success in your section of the market. Furthermore, expanding your knowledge of data analysis techniques can benefit you. According to Statista, 87.9 percent of companies surveyed in 2023 claim that investing in data analytics is a high-level priority [1]. 

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 methods of topic modeling help to analyze a collection of text by locating and grouping words based on their frequency of use. By doing so, this natural language processing (NLP) 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 topics based on their relationship to each other. The data is sorted into three levels, those being topic, word, and document. For example, this technique might come up with ‘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 main principles; every document is a mixture of topics, and every topic is a mixture of words. Once words are grouped by topic, the amount of times those words and topics occur helps to make a document matrix that creates an interconnected network that classifies the data.

Probabilistic latent semantic analysis (pLSA)

By analyzing the word co-occurrence, pLSA uses probability to model the connections between words and topics, as well as between topics and documents. You can use the pLSA method 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 group together in the context of the text. It scans unstructured data to locate previously hidden relationships. The algorithm places this information on both a topic-term and document-topic matrix. Each cell then represents the amount 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 multiple words appear in the text that share the same meaning.

For example, if you’re a medical professional, you 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 that exist 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 on its own by grouping words by relevance and recurrence. It finds common themes and groups those words into a cluster. For example, the topic modeling method might identify certain documents as contracts while labeling others, depending on the themes, 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. Below, you can find a more detailed look at 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, which means they have the opportunity to adjust messaging based on customer needs.

Medical researchers

Medical researchers use topic modeling for medical document data mining. For example, it might help you group gene sequence data, or you can use it to 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, as well as discover any recurring issues that need addressing. For example, you might use topic modeling to group similar products together on your website to help customers find more items they might show interest in. You can also group customer support, ensuring they pass to the right team members quickly.

Pros and cons of topic modeling

Topic modeling comes with its benefits, such as hidden-topic and sentiment identification. However, it also contains some drawbacks, like narrow parameters or faulty grouping. Here, you can take a closer look at the pros and cons:

Benefits

Topic modeling visualization takes the mundane tasks of sorting through heaps of unstructured data and makes it 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 parameters that are overly specific or don’t group the words in an optimum way. It can also struggle to understand the difference between words within the same topic due to overlooking the contextual clues. This often results in a professional having to interact 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 make sure you have a strong foundation in mathematics. Online courses, videos, or articles all help to increase your knowledge of statistics and linear algebra. Then, ensure you have 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 feel you have the necessary knowledge, build a portfolio that showcases your expertise and seek out entry-level roles that include topic modeling as an expected task.

Finally, if you’re interested in working in this field, a machine learning engineer can earn a substantial salary. According to Glassdoor, the median annual salary for this position is $121,250 [2].

Learn more about topic modeling on Coursera

Topic modeling is a natural language processing technique that allows an algorithm to seek out topics and words and group them by relevance. If you’d like to discover more about topic modeling, explore the courses and Professional Certificates available on Coursera. With options such as the University of Colorado Boulder’s Unsupervised Text Classification for Marketing Analytics, you’ll get to explore the foundations of topic modeling.