Text Mining

What is Text Mining?

Text mining (also known as text analysis), is the process of transforming unstructured text into structured data for easy analysis. Text mining uses natural language processing (NLP), allowing machines to understand the human language and process it automatically.For businesses, the large amount of?data generated every day?represents both an opportunity and a challenge. On the one side, data helps companies get smart insights on people’s opinions about a product or service. Think about all the potential ideas that you could get from analyzing emails, product reviews, social media posts, customer feedback, support tickets, etc. On the other side, there’s the dilemma of how to process all this data. And that’s where text mining plays a major role.

Like most things related to?Natural Language Processing?(NLP), text mining may sound like a hard-to-grasp concept. But the truth is, it doesn’t need to be. This guide will go through the basics of text mining, explain its different methods and techniques, and make it simple to understand how it works. You will also learn about the main applications of text mining and how companies can use it to automate many of their processes:

1. Getting started with text mining
2. How does text mining work?
3. Use cases and applications

Let’s jump right into it!

Getting Started With Text Mining

Text mining?is an automatic process that uses?natural language processing?to extract valuable insights from unstructured text. By transforming data into information that machines can understand, text mining automates the process of classifying texts by sentiment, topic, and intent.

Thanks to text mining, businesses are being able to?analyze complex and large sets of data?in a simple, fast and effective way. At the same time, companies are taking advantage of this powerful tool to reduce some of their manual and repetitive tasks, saving their teams precious time and allowing customer support agents to focus on what they do best.

Let’s say you need to examine tons of reviews in?G2 Crowd?to understand what customers are praising or criticizing about your SaaS. A text mining algorithm could help you identify the most popular topics that arise in customer comments, and the way that people feel about them: are the comments positive, negative or neutral? You could also find out the main keywords mentioned by customers regarding a given topic.

In a nutshell, text mining helps companies make the most of their data, which leads to better data-driven business decisions.

At this point you may already be wondering, how does text mining accomplish all of this? The answer takes us directly to the concept of machine learning.

Machine learning is a discipline derived from AI, which focuses on creating algorithms that enable computers to learn tasks based on examples. Machine learning models need to be trained with data, after which they’re able to predict with a certain level of accuracy automatically.

When text mining and machine learning are combined, automated text analysis becomes possible.

Going back to our previous example of SaaS reviews, let’s say you want to classify those reviews into different topics like?UI/UX,?Bugs,?Pricing?or?Customer Support. The first thing you’d do is train a topic classifier model, by uploading a set of examples and tagging them manually. After being fed several examples, the model will learn to differentiate topics and start making associations as well as its own predictions. To obtain good levels of accuracy, you should feed your models a large number of examples that are representative of the problem you’re trying to solve.

Now that you’ve learned what text mining is, we’ll see how it differentiates from other usual terms, like text analysis and text analytics.

Difference between Text Mining, Text Analysis, and Text Analytics?

Text mining and?text analysis?are often used as synonyms. Text analytics, however, is a slightly different concept.

So, what’s the difference between text mining and?text analytics?

In short, they both intend to solve the same problem (automatically analyzing raw text data) by using different techniques. Text mining identifies relevant information within a text and therefore, provides qualitative results. Text analytics, however, focuses on finding patterns and trends across large sets of data, resulting in more quantitative results. Text analytics is usually used to create graphs, tables and other sorts of visual reports.

Text mining combines notions of statistics, linguistics, and machine learning to create models that learn from training data and can predict results on new information based on their previous experience.

Text analytics, on the other hand, uses results from analyses performed by text mining models, to create graphs and all kinds of?data visualizations.

Choosing the right approach depends on what type of information is available. In most cases, both approaches are combined for each analysis, leading to more compelling results.

Methods and Techniques

There are different methods and techniques for text mining. In this section, we’ll cover some of the most frequent.

Basic Methods

Word frequency

Word frequency can be used to identify the most recurrent terms or concepts in a set of data. Finding out the most mentioned words in unstructured text can be particularly useful when analyzing customer reviews, social media conversations or customer feedback.

For instance, if the words?expensive,?overpriced?and?overrated?frequently appear on your customer reviews, it may indicate you need to adjust your prices (or your target market!).

Collocation

Collocation refers to a sequence of words that commonly appear near each other. The most common types of collocations are bigrams (a pair of words that are likely to go together, like?get started,?save time?or?decision making) and trigrams (a combination of three words, like?within walking distance?or?keep in touch).

Identifying collocations — and counting them as one single word — improves the granularity of the text, allows a better understanding of its semantic structure and, in the end, leads to more accurate text mining results.

Concordance

Concordance is used to recognize the particular context or instance in which a word or set of words appears. We all know that the human language can be ambiguous: the same word can be used in many different contexts. Analyzing the concordance of a word can help understand its exact meaning based on context.

For example, here are a few sentences extracted from a set of reviews including the word ‘work’:

Advanced Methods

Text Classification

Text classification?is the process of assigning categories (tags) to unstructured text data. This essential task of?Natural Language Processing?(NLP) makes it easy to organize and structure complex text, turning it into meaningful data.

Thanks to text classification, businesses can analyze all sorts of information, from emails to support tickets, and obtain valuable insights in a fast and cost-effective way.

Below, we’ll refer to some of the most popular tasks of text classification – topic analysis, sentiment analysis, language detection, and intent detection.

• Topic Analysis:?helps you understand the main themes or subjects of a text, and is one of the main ways of organizing text data. For example, a support ticket saying?my online order hasn’t arrived, can be classified as?Shipping Issues.
• Sentiment Analysis:?consists of analyzing the emotions that underlie any given text. Suppose you are analyzing a series of reviews about your mobile app. You may find out that the most frequently mentioned topics in those reviews are?UI-UX?or?Ease of Use, but that’s not enough information to arrive to any conclusions. Sentiment analysis helps you understand the opinion and feelings in a text, and classify them as positive, negative or neutral. Sentiment analysis has a lot of useful applications in business, from analyzing social media posts to going through reviews or support tickets. In terms of customer support, for instance, you might be able to quickly identify angry customers and prioritize their problems first.
• Language Detection:?allows you to classify a text based on its language. One of its most useful applications is automatically routing support tickets to the right geographically located team. Automating this task is quite simple and helps teams save valuable time.
• Intent Detection:?you could use a text classifier to recognize the intentions or the purpose behind a text automatically. This can be particularly useful when analyzing customer conversations. For example, you could sift through different outbound sales email responses and identify the prospects which are interested in your product from the ones that are not, or the ones who want to unsubscribe.

Text Extraction

Text extraction?is a text analysis technique that extracts specific pieces of data from a text, like keywords, entity names, addresses, emails, etc. By using text extraction, companies can avoid all the hassle of sorting through their data manually to pull out key information.