Ever heard of Text Mining? This term is increasingly present in the market, which can be your company’s differential. Text Mining means “text mining.” And to understand the meaning of this, you can think of the act of extracting ores from nature.
As long as the metal is raw, there isn’t much use for it in industry. But when processed this raw material is transformed into items and productive activities by human hands.
Likewise, in the textual content, there are several pieces of data that, if processed, can yield important information for the company to develop better products and services. In addition, it favors the provision of adequate procedures for each consumer. Want to understand how this works? See the details of the subject by reading this post!
What Is Text Mining?
Text Mining or text mining is a process in which unstructured texts are transformed into actionable and meaningful information. Identifying topics, keywords, and patterns makes it possible to obtain information without the need for manual analysis. Interesting.
With text mining, it is possible to analyze large and complex customer data efficiently, quickly, and simply. Knowing this, many companies are taking advantage of the opportunity to reduce repetitive and manual tasks, giving teams more time for what matters.
Think, for example, that you will examine the reviews to understand your system’s main targets. With a mining algorithm, you can identify the most popular topics and how people feel when referring to your brand or your offers.
Do the comments tend to be more positive, negative, or neutral? Finding the keyword and understanding the users’ point of view is also possible. Questions like these show that mining is useful to help companies understand and use their data to their full advantage, which favors better business decisions.
For this to happen, working with a machine that can learn is necessary. Machine Learning is derived from Artificial Intelligence and indicates that algorithms can acquire knowledge from examples from databases. The machine retains new learnings just like a human — after understanding a few examples, it can work with greater speed and maintain the desired level of accuracy.
What Are The Differences Between Text Mining And Text Analytics?
Both text mining and text analysis work with similar problems. Therefore, confusions are common. But as the techniques are different, you will now understand the differences.
Text analysis is developed from computational linguistics. With a series of rules, it is possible to encode human understanding with high precision. The problem happens when new situations are tested, which are fragile and adapt slowly.
On the other hand, text mining has been used for less time. It arose from using statistics, data mining, and machine learning. It has the power to create models from the use of previous data. For this, training is required. With it, the machine can adapt to scenarios, remembering how it solved similar problems.
So the performance is equivalent. Whereas text analysis needs a linguist to produce complex rules, mining requires analysts to make manual labels for training. Therefore, the strategies show even more efficient results combined than when acting alone.
How To Know If A Company Needs Text Mining?
Text mining can simplify the analysis of large-scale raw data, transforming it into useful business information. This gives you multiple opportunities to make teams more productive by automating tasks and making quick, technical decisions.
Therefore, the possible applications in text mining are endless and can be used in different sectors such as marketing, customer support, sales, and product.
As an exercise, you can think of all the repetitive and manual tasks the team has to deal with daily. Imagine now if this were no longer necessary. How would time be invested to improve results?
You could think of a more efficient team of professionals who could focus on the tasks they do best. It can offer customized and quick solutions, leaving repetitive demands on the machine.
