Topic modeling is an unsupervised machine learning technique. The two most common approaches for topic analysis with machine learning are NLP topic modeling and NLP topic classification. Topic analysis uses natural language processing (NLP) to break down human language so that you can find patterns and unlock semantic structures within texts to extract insights and help make data-driven decisions.

Topic analysis (also called topic detection, topic modeling, or topic extraction) is a machine learning technique that organizes and understands large collections of text data, by assigning “tags” or categories according to each individual text’s topic or theme. Let's get started! Introduction to Topic Analysis Topic Analysis Use Cases and Applications Read this guide to learn more about topic analysis, its applications, and how to get started with no-code tools like MonkeyLearn. The good news is that AI-guided topic analysis makes it easier, faster, and more accurate to analyze unstructured data. Manually sorting through large amounts of data is more likely to lead to mistakes and inconsistencies.

It’s also tedious, time-consuming, and expensive. When it comes to analyzing huge amounts of text data, it’s just too big a task to do manually.

Businesses deal with large volumes of unstructured text every day like emails, support tickets, social media posts, online reviews, etc.

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