Everyone has had those pieces of text that they absolutely agonize over. Think of how many ways you analyze that email from a recruiter for a job you really want. Or all the people you ask to read a text from your crush to try to decipher if they like you or not.
Words are powerful, and they can have so many different meanings. It takes great sleuthing skills and analysis to try and figure out what exactly those messages mean. If you’ve ever done this and agonized over certain pieces of text, you’re not alone.
People aren’t the only ones who use text as data to further an agenda. In fact, companies and organizations do this all the time.
Through text mining and analytics, different businesses and agencies can gain insights into the customer response and opinions throughout the internet. People love speaking their minds and publishing findings on social media, customer reviews, blogs, and more.
When an organization can harness that text, they gain greater insights into the minds of their target audience. Let’s dive into the definition of text analysis and some of the steps to this business intelligence software.
What are text analysis and text mining?
Let’s start with a basic definition of textual analysis software. Text analytics is a data science platform that uses machine learning and statistical/linguistics techniques to transform unstructured text and data into patterns and insights that help make predictions for your organization.
This helps businesses, government entities, researchers, and more find crucial pieces of information from a large amount of unstructured data. Use these insights to make better business decisions and form a strong organization overall.
What are the steps to text analytics?
Text analytics involves a few different steps. You start by text gathering. With so much data and text on the internet, you’ll need to look for ways to combine all those details.
Comb through customer reviews, social media posts, blogs, news articles, and online forums to find specific data sets that are relevant to your brand.
Once you have the information, you can transform it into set data silos and prepare the text for visualization and understanding. From there, machine learning will take over, and you can perform your predictive analytics.
Gain accurate insights and forecast future decisions when you have the right tools and techniques derived from textual analysis.
There are a few different uses and text-analytics techniques that can affect your organization. Let’s take a look at a few of those use cases.
Sentiment Analysis
A lot of text on the internet can be labeled with a certain emotion. This is known as sentiment analysis. Start by breaking down your information into positive and negative sections.
You can also dive deeper and understand further reviews that may be angry, confused, or disgusted. The more in-depth you can go, the better insights you’ll gain in the long run.
Topic Modeling
Sometimes you just want to organize all your information into sections on specific topics. This is known as topic modeling.
Filter large amounts of data into specific silos and visualizations when you use algorithms designed to find specific keywords and subjects. Group together sections of research or find new patterns in large sets of data when you utilize this technique.
Name Entity Recognition
If you’ve ever used the “command F” feature on your computer to find specific words in an essay or article, you are essentially using a form of Name Entity Recognition.
This is a text analysis technique that helps you identify people, places, or things throughout the internet. See specific locations or gain insights on exact organizations with this technique.
