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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 exist, such as natural language processing (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 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 Statista, 87.9 percent of companies surveyed in 2023 claim that investing in data analytics is a high-level priority [].

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鈥檚 topic is. It finds the structure within a data set by grouping words into issues based on their relationship to each other. The data is sorted into three levels: topic, word, and document. For example, this technique might come up with 鈥榖iology鈥� as a topic for a document and then assign words such as 鈥榞enus鈥� or 鈥榗arnivore鈥� 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 to 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.听

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 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 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鈥檛 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 annual salary for this position is $119,205 [].

Pros and cons of topic modeling

Topic modeling has benefits like hidden topic and sentiment identification. However, it also has drawbacks, like 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鈥檚 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鈥檒l 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.

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