Text analysis, also known as text mining, is the process of automatically classifying and extracting meaningful information from unstructured text. It involves detecting and interpreting trends and patterns to obtain relevant insights from data in just seconds.
Let’s say your team needs to analyze hundreds of online reviews to learn about the things that your clients like or dislike about your product. Reading each review manually would be time-consuming and highly ineffective. Text analysis, however, can help you automatically tag reviews according to their topic (topic analysis) and classify each opinion as positive, negative, or neutral (sentiment analysis), saving your team valuable hours and ensuring consistent criteria is applied to all your data.
To understand how text analysis works, it’s important to mention Natural Language Processing (NLP), a subfield of Artificial Intelligence that helps computers understand how we communicate. Think of it this way; while text analysis tools are analyzing data, NLP is enabling them to do so by applying language-deciphering techniques in the background.
Another term you may have heard is text analytics. While it’s closely related to text analysis, the difference is that text analysis obtains qualitative insights from unstructured text, while text analytics uses data visualization tools to transform insights into quantitative data and tells the story behind the numbers.
Companies deal with huge amounts of data every day, from emails, chats and social media posts, to customer support tickets, and survey responses. However, 80% of this data is unstructured and, therefore, hard to process. Text analysis offers a solution to this problem and makes it possible to detect unseen connections and similarities in large collections of data, providing businesses with relevant insights they can use to enhance their decision-making.
For example, text analysis can help you analyze customer feedback, like open-ended responses in NPS surveys, and make improvements based on first-hand information from your clients. If your customer service receives low scores, for instance, text analysis can be used to dig deeper into the reasons for these results, allowing you to know exactly what is going wrong. Maybe your customers were expecting to reach a person instead of a machine whenever they need assistance, or maybe they were kept waiting too long before getting their first response. Knowing the why behind your stats and scores allows you to make smart decisions that increase customer satisfaction.
Businesses can also use text analysis to automate internal tasks that teams carry out manually. In customer service, for example, text analysis can help you automate your ticket tagging process and route tickets to the most appropriate agent. This makes companies more efficient, by saving agents valuable time and allowing them to focus their talents on delivering better customer experiences.
There are two main text analysis models: text classification and text extraction. Text classification consists of assigning predefined tags or categories to a text, based on its content. Text extraction, on the other hand, is the process of extracting relevant information like keywords, company names, prices, and product specifications from unstructured text. Each of these models contains different applications, some of which we’ve mentioned below:
Topic analysis: this text classification model identifies frequent themes or topics in a text. You can use a topic classifier to categorize incoming support tickets, product reviews, and NPS responses, among other types of text.
“I love this app! It’s very intuitive: it took me just a few minutes to set it up” → this product review would be tagged as Ease of Use.
Sentiment analysis: identifies the subjective information in a text and classifies opinions as positive, negative, or neutral. You can use it to analyze Twitter mentions, customer support interactions, survey responses, and more, and get insights on how your customers feel about your brand.
Language Detection: A language detector automatically classifies a text based on its language. This can be very useful for ticket routing, for example, if you’re an international company you can route tickets to localized teams that understand them.
Intent Detection: this classifier detects the intention behind a text, allowing you to take action immediately. For example, you might receive emails that request to unsubscribe from your product or a message that demonstrates an interest in your product. By classifying these into intents, such as Unsubscribe and Interested in Product, you can take immediate action.
Getting started with text analysis is not as difficult as it sounds. In fact, there are many online tools available that don’t require any programming skills to begin with.
An AI platform like MonkeyLearn, for example, enables you to access several pre-trained machine learning models that can help you analyze your data right away. If you want to check out how it works, just paste or write a piece of text in this pre-trained sentiment analysis model and click on ‘Classify Text’. You can also use one of our many integrations to connect to your favorite apps, so you can import data from, let’s say, Excel, Google Sheets, Zapier or Zendesk, in just a few clicks.
If you need a machine learning model tailored to a specific industry, the best option for you would be to build a customized model for text classification and text extraction. For more information on how to build your own classification model, check out this article.
Whether you decide to use one of our pre-built models or create your own, you can put your machine learning model to work with your own data by analyzing data in a batch, connecting with any of the available integrations with third-party apps, or using the MonkeyLearn API.
Text analysis transforms unstructured data into qualitative actionable insights, helping companies make smart data-driven decisions. Thanks to classification and extraction models, it’s now possible to find relevant information in just a few seconds. Text analysis can also improve the efficiency of business areas like customer service, by automating tasks that otherwise would have been done manually. It can also help businesses become customer-centric by paying closer attention to their customers’ needs and detecting problems quickly to deliver more relevant responses.
There are many online tools available for text analysis, and it can be easy to get started. MonkeyLearn, for example, doesn’t require any setup and offers a variety of smart text analysis tools that can analyze your data in seconds.
Want to see how it works? Sign up for free and test your first text analysis model right away!
November 13th, 2019