How Text Mining Helps You to Build a Better Business Strategy

Text analytics or text mining is the technique that allows you to use machine learning in order to automatically process large volumes of textual data. It is important for any business to keep an eye on customer reviews, client feedback, and brand mentions on social media.

Text mining is a powerful type of automated research program that uses artificial intelligence and natural language processing to retrieve text from the web and extract meaning from it. The idea is pretty simple – take bits and chunks of text, classify them into related groups, and connect them with other pieces of information. Text mining is a relatively new idea in data mining, but it’s already advanced in its application.

You can then use artificial intelligence to analyze and structure the results. Text mining has been around since the late 90’s but recently AI has become more powerful and capable. Text mining has already been applied to extract location data, search terms, and even campaign-related tweets. Recently the technology has been extended to extract information from every part of a website including images, meta data, audio files, and HTML code. Its ability to extract meaning allows it to be used for other purposes such as providing better user experiences.

Below we’re going to talk about 5 ways how text analytics can benefit your business.

Sentiment analysis

img source:

Sentiment analysis is also often called opinion mining. In order to understand how customers perceive your goods and services, you can use natural language processing and computational linguistics. Certain machine learning algorithms allow data analysts to extract meaning from textual data, primarily about what concerns affective states and subjective opinions.

For example, you can implement a sentiment analysis system to mine and analyze hundreds of thousands of customer reviews all over different platforms. Timely reacting to negative sentiment is important to keep your company afloat. Moreover, you can get some valuable information for improving and developing your products. If you want to learn more about how textual sentiment analysis can be used for business, check out this article about NLP and text analysis techniques.

Customer service

Text analysis tools enable your company to process customer support tickets much faster and, therefore, solve any potential problems clients have more effectively. Once you get a request, it is saved in your CRM system. ML software scans for keywords classifying the problem and then assigns the ticket to a customer support manager. Automation allows you to eliminate unnecessary manual work for your employees allowing them to concentrate on helping customers.

If you want to bring that process to a new level, you can implement a chatbot. A virtual assistant uses natural language processing to interpret the request and provide a solution to a standard problem. Only when there is a need, the request is sent to a human professional.

Influencer marketing

img source:

More and more businesses today recognize the influence that bloggers on different social media have on people. Working with influencers can add authenticity to your marketing campaigns – people tend to trust the advertisement from the favorite blogger more than a TV ad or targeted campaigns.

In order to come up with content plans and hashtags, you need to research what your competitors are using and who they work with. Special social marketing tools that use text analysis make this process much easier. They also allow you to uncover trends and boost your chances of creating viral content.


Optimizing your website for SEO is essential if you want potential clients to discover you. The majority of users don’t go further than the first page in search results, therefore, your webpage needs to be in the top 10. You can use text analysis to define your keywords.

Use text mining tools to analyze the content of your competitors, search for important keywords, and use this list to insert in blog posts, meta-tags or permalinks. While Google algorithms today have evolved and the quality of your content has never been as important, correct keywords and SEO strategies can contribute to your success.

Commercial info

img source:

One of very important and time consuming operations, related to the commercial sector is the control of the retail prices. It’s becoming a night mare if your company is the representative of the brand, working in several states, or even nationwide. Text mining allows crawling websites in order to retrieve information automatically – such as a list of SKUs or prices of products. The most fascinating fact is that updating the collection of data demands no effort from your side. Once trained, the machine learning model will be scraping the information, extract updated figures, and provide you with the actual data at the predefined regularity.

The recent research on extracting meaning from unlabeled Twitter data demonstrated the feasibility of applying text mining to generate richly spatio-temporal representations of large volumes of public Twitter metadata. One more example of introduction data mining to support the HoReCa:  over 2.5 million restaurants across the US were indexed, data about the general rating, average bill, and the level of visitors’ satisfaction has been extracted and updated on a regular basis.  In the future updates there will be more indicators and features.

As you can see, text mining is extremely important for your business in the domains of PR, customer service, and marketing. Text mining is an extremely powerful tool that can be used for many different purposes. The main thing you need to know about text mining before deciding whether or not to use it on your project is that it’s an incredibly visual and analytical process. It requires a great deal of legwork and planning in order to do well, so you should always get extra help from someone trustworthy if you’re looking for that extra edge. If you feel like your company lacks a text analytics strategy, feel free to discover off-the-shelf ML tools for text analytics or develop your own from scratch working with a professional development team.