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Topic Modeling: Extracting Themes and Patterns from Textual Data

Aleksandra Tadrzak

9 min read

Apr 10, 2024

If you spent any time on the internet in the last 12 months, you’re of how artificial intelligence (AI) has captured the world’s imagination. Churning out masterful artwork in seconds and carrying on authentic, human-like conversations is just the tip of the iceberg. AI is also poised to drastically transform the way we do business.

For example, enterprise software has been enhanced with AI integrations that improve its analytical features. One of the main areas of improvement has been topic analysis and its subcategory, topic modeling.

Have you started incorporating topic modeling into your business processes yet? If not, you’re falling behind! This article will delve into the nature of topic modeling, how it works, and why businesses need to adopt it sooner rather than later. It will also cover other topic analysis methods, like topic classification and clustering.

Let’s begin!

Topic modeling: An analytical advantage

It’s no secret we live in a data-driven world. Learning to read data and draw actionable insights from it can give businesses an advantage in a competitive market. But how do you make sense of data when there’s so much to analyze?

It’s estimated that 328.77 million terabytes of data are created every day, and nearly 90% of the world’s existing data was generated in the last two years alone! It’s just not humanly possible to work with such daunting and enormous quantities of data.

The amount of data isn’t the only problem. Data comes in both structured and unstructured forms. While structured data is easier to work with and analyze, unstructured data is generated at a much faster rate. A Gartner report found that 80% to 90% of new enterprise data is unstructured.

When there are literally zettabytes—one zettabyte equals one trillion gigabytes—of data to analyze, manual methods become more of a hindrance than an advantage. That’s why topic modeling has become such an important area of interest in AI technology.

What is topic modeling?

Topic modeling is an unsupervised machine learning (ML) technique that can analyze text documents to discover clusters or groups of semantically similar words within a corpus. Since it is an unsupervised form of ML, topic modeling doesn’t require a large body of training data to function. Instead, it uses AI technologies like text mining, statistical modeling, and natural language processing (NLP) to find the common themes and clusters of words in a document.

As the volume of enterprise data grows beyond anyone's capacity to analyze, topic modeling provides a more reliable alternative. It can analyze data faster than a person while also delivering error-free results. If you want to understand your business enterprise data better and turn it to your advantage, topic modeling will help you work through the entire corpus. It lets you learn from unstructured data while performing real-time analysis on it simultaneously.

How does topic modeling work?

There’s no doubt that topic modeling is a powerful tool for businesses. But how does it work? Like many AI-based business solutions, while its results are easy to see, the inner workings of topic modeling are more complex. In layman’s terms, topic modeling scans documents, reads the words they contain, and groups similar words together to identify topics.

In topic modeling, a topic is simply a descriptor for a text corpus. The AI works to establish relationships between topics and certain words. Then, by tracking the familiarity with which those words appear in the text, it determines the topic or topics the document contains.

The two main methods of topic modeling

There are two main techniques that enable AI to conduct topical modeling: latent semantic analysis (LSA) and latent dirichlet allocation (LDA). Both techniques rely on NLP to process unstructured text data, but their methods are different. Let’s examine these in closer detail.

Latent semantic analysis

If you want to discover the obvious and hidden topics within the corpus, you can rely on LSA. The foundation of LSA is the idea that semantically similar words are often used together in context. That principle informs the assumption that documents with similar topics will have roughly the same distribution frequency of certain words. LSA can be used to discover topics within a single text document or an entire corpus of text.

Latent dirichlet allocation

The objective of LDA is to assign a topic to every word (or a majority of words) in a document, thereby creating a list of topics it contains. These topics are used to create a document-term matrix, which represents the distribution of topics in the document.

For example, LDA will represent a given document as a collection of topics in proportion, like 25% topic A, 45% topic B, and 30% topic C.

LSA vs. LDA: The key difference

Both LSA and LDA share similarities in how they are unsupervised machine learning methods that use NLP and are both useful and practical approaches to topic analysis. However, the main distinction between the two is their intended purpose. While LSA seeks to discover the relationships between the words in a document, LDA aims to uncover the latent topics contained in a text.

Various aspects of topic modeling

Now that you know how topic modeling can transform unstructured data into structured breakdowns of relevant themes, its value will become more evident. There are many facets to topic modeling, each offering a different advantage. Let’s go over the multiple aspects of topic modeling and their significance.

Discovering hidden topics

It’s possible for human analysts to occasionally overlook a topic that isn’t made explicit in the text. This is even more likely when working with a large corpus. But topic modeling algorithms don’t make those types of mistakes. Even topics that are hiding in gigantic text databases can be immediately identified.

Efficient document organization

Sorting an extensive collection of documents into defined categories is time-consuming when performed manually. When working with unstructured data, it takes even longer. But topic modeling algorithms can easily categorize documents by assigning them topics and sorting them accordingly.

Greater semantic understanding

Rather than simply identifying keywords, topic modeling goes beyond and examines each word in a document in context. For example, the word “fair” might mean two very different things depending on the words that come before and after it. A “fair judge” and a “village fair” are two very different things that use the same word. Topic modeling understands context, which leads to a much more nuanced analysis of text data.

Dynamic topic modeling

Human knowledge continually grows as we make discoveries and formulate new theories. This means our understanding of topics and the manner in which we discuss them is continuously evolving. For example, the topic “Car Features” had the terms “power steering” and “automatic braking system” for years now but has recently expanded to include the terms “self-driving” and “smart car."

Dynamic topic modeling is extremely useful in tracking the trends and patterns in understanding a given topic.

Cross-cutting applications

A tool as powerful as topic modeling can add value wherever it is used. It’s a versatile technology that is applicable across domains, including:

Analyzing customer feedback
Social listening
Reviewing news articles and other media

These are just a few examples of the many applications of topic modeling. As the technology continues to improve, more use cases for topic modeling will emerge.

Improving information retrieval

The faster your organization can access vital information, the greater the chances of success. Since topic modeling sorts documents into neat categories, information becomes much easier to access. Topic modeling greatly enhances the efficiency of information retrieval systems, especially where unstructured data is concerned.

Content summarization

With the power of NLP, topic modeling algorithms can clearly understand a text document's contents. This knowledge can then be used to summarize the document by listing its key topics and offering a concise overview of the content’s main ideas.

Integrating sentiment analysis

Another outcome of NLP is that topic modeling yields a more comprehensive understanding of text data. It doesn’t just identify the topics of text data; it can also guess the emotional undercurrent for each of them. By reading the context, topic modeling algorithms can tell you how the author of a particular document felt about the topics they wrote about.

Discovering trends and patterns

When analyzing a large corpus, topic modeling can identify emerging trends, recurring patterns, and shifts in discourse. For example, using topic modeling to analyze the comments on a brand’s latest social media advertisement will allow the business to track how different demographics respond to the ad. This ability to identify influential changes in real time, if not beforehand, is a great competitive advantage that aids strategic decision-making and trend forecasting.

Interactive topic visualizations

Once the topic modeling algorithm imparts structure to unstructured data, the results can be represented in a number of interactive visual formats. Using charts, graphs, and diagrams helps break down complex and layered textual data into an easily understandable form. By simplifying how text data is represented, topic modeling becomes a user-friendly method of exploring and analyzing data.

Limitations of topic modeling

Despite all these evident advantages, topic modeling has its own share of challenges. Some of the obstacles to the smooth functioning of topic modeling are:

Dealing with overlapping topics that share similar words, like “car race” and “bike race" or “chemistry” and “biology".
Difficulty in accurately identifying topics for short-form content.
Topic modeling algorithms require extensive configuration.
Biases may be acquired from human operators.
Insufficient data might lead to ambiguous results.

Even though these challenges are inherent in topic modeling, there are ways to work around them. Other analytics methods like topic classification or clustering can be employed in scenarios where topic modeling doesn’t seem like the right solution.

Ultimately, deciding what kind of topic analysis you should use comes down to your business needs.

Get to know your customers with AI topic modeling

Customer service and support is one business operation that stands to gain the most from effective topic modeling. It helps route tickets to the right agents, sort them according to urgency, and judge the emotional tone of their messages.

HelpDesk is a convenient customer service software product that adds value by automating customer service tasks, like sending out mass messages and delivering real-time reports. Unlock the power of topic modeling for your customer service operations and watch your team’s performance reach the next level.

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