There are millions and millions of words and text that exist on the internet. With so many people having unfettered access to the World Wide Web, it’s only natural that a lot of text data ends up out in the stratosphere. For a business owner, however, that unstructured data could easily turn into fuel for your database. Find ways to improve your overall operations when you utilize data mining and text analysis.

Information extraction can offer you all the insights you need to take your organization to the next level. Use text analytics to see patterns and identify common metrics throughout the web. By combing through natural language, a text mining software can get relevant information from social media, blog posts, customer reviews, and more. Simple phrases or keywords are valuable pieces of information for your data scientists. When you perform a systematic review and computational linguistics with these datasets, you are setting your team and organization up for success. Use these insights and text-mining techniques to make smarter decisions for your business and customer-relationship management. Let’s look at some examples of text mining and how you can read the unstructured text in different ways.

What are text mining and text analytics?

Text mining, also known as text analytics, is a data-science technique that utilizes machine learning and logistics processing to turn unstructured text into a format that displays insights and patterns. With such an enormous amount of content out there, text mining algorithms search these datasets to help businesses, government entities, researchers, and the media to understand better analytics. Discover models and bold predictions by monitoring and creating clusters of data and text. These models can then be deployed to test new systems and check out your good results. By breaking down average text into specific information, you can get a better summarization of your internet presence and gain insights from structured data. There are a few different techniques and examples of text mining. Let’s dive into those now.

Sentiment Analysis

One of the first text mining applications involves sentiment analysis. This is the process of deciphering emotions and attributes from the text. By reading customer reviews and seeing how certain products are being received, you can use specific metrics to read future trends. Go with a polarity approach and simply read positive or negative reviews or chose to dive deeper with fine-grained analysis. This will help you understand trends and track customer responses over time.

Topic Modeling

With so many pieces of text on the internet, there’s bound to be a lot of overlap. Topic modeling is a form of text categorization that groups new information together. Understand similarities in data and text for research purposes or to gain insights on new trends. Law firms can use this text to combine areas they’re studying for litigation, and businesses can use it to see fresh topics online. This technique helps you turn a large dataset into decipherable data silos and citations.

Named Entity Recognition

Named entity recognition (NER) is a technique that helps you mine the text for specific names and nouns. If you’re looking for a person, place, or organization, you can get that information with NER capabilities. Separate your news based on specific topics or look for a certain region within your chain organization. Find a needle in a haystack when you recognize certain names and organizations within large data sets.

Event Extraction

To get even more specific, you can use event extraction to group text and data by specific moments. If an event occurslike mergers, acquisitions, or important meetings, you can get specific data on those text classifications. Every little moment affects your business, and this allows you to see that more specifically.