Text mining is gaining useful information from text by Artificial Intelligence (AI). It uses NLP, which is short for Natural Language Processing, to convert unstructured data into structured data. This is needed for analysing and for machine learning (ML) algorithms.
It is used by many different types of companies for all sorts of purposes, such as analysing, research, decision-making based on data etc. We have our own expanding scenarios; and further examples for what it can be used for are:
Social media comments
So now that we clarified text mining in two short paragraphs, we are done, and this post is over, class dismissed, right?
Not so much.
First of all, let’s explain what NLP and ML are, because without that, this little intro will mean close to nothing to you, and you came here to learn about text mining. We will get there!
NLP or Natural Language Processing includes Natural Language Understanding and Natural Language Generation. The aim is to help our beloved machines understand text in a natural language, such as English. Generation, however, takes it a step further, and creates the text in a natural language.
The best examples are all the talking and listening devices around us that developed rapidly over the past decade: Siri, Google, Alexa. They are capable of understanding requests said to them (using our natural language in our own voice, said out loud) and answering to us with relevant (or not so much – try asking them to tell you a story and you’ll be surprised!) info, aka generating it.
Machine learning is basically what it sounds like: your machine learning, of course, in a mental understanding. It is used to teach tasks by examples. You need to feed the algorithm many examples so that it can predict the rest automatically, hopefully with a minimum amount of mistakes. Basically the machine learns from experience, without you needing to program everything.
Combine text mining with NLP and voila, you got the possibility of automated text analytics.
What’s the difference between text mining and text analytics?
Text analytics differs from text mining.
Text mining is used for qualitative results, while text analytics is for quantitative outcomes. Simply said: if you want to see numbers and graphs, you need a quantitative research, whereas qualitative results are descriptive, and needs observation, not measurements.
It’s safe to say that text analytics often build on text mining. They both used for the automation of analysing texts, but “text analytics …uses results from analyses performed by text mining models, to create graphs and all kinds of data visualizations.” (https://monkeylearn.com/text-mining/)
Combine both to get the best results in understanding massive amounts of data!
The importance of text mining
We generate an unbelievably large amount of data daily. You wake up, pick up your phone, and start scrolling through some of your applications. You visit your regular pages, leave a comment here and there, like some things. Then you go on to opening your emails, and start answering to every business matter, swapping your email app to a messaging one to answer to some of your friends and have a little chat as a break in between.
All this is unstructured, raw data. Without automated text mining, it is useless, because it’s unsearchable, there are no patterns identified, no keywords gained. Just blah, blah, and a bit more blah.
But once you start text mining and organising this data, suddenly it becomes something useful for your company. You can check your customer feedbacks, and screen through the words they’ve used the most, making it easier to decide how to change the communication, or even prices. This, of course, is not all, you can automatically screen through and categorise all the orders you’ve received, what they say about you on social media, what’s the topic mentioned most often in your emails, and the list goes endless.
You can make your company more effective with automation of the most draining manual tasks, and free your employees’ mind from the dull jobs, no matter what field you’re working in.
Contact us, and we can help you figure out how HubScience will be your best investment this year!