Chances are, if you work in an organization, you are going through huge amounts of unstructured data on a daily basis. This can be in any shape and form - emails, tweets, social media comments, marketing copy, customer support tickets, survey responses, or IMs.
It's also likely you are still processing these (semi-)manually - which is time-consuming, tedious, and prone to mistakes.
But we have some good news - there are hundreds of ways to analyze your text data at scale.
So, let’s explore how text analysis can help you grow your bottom line, look at some text analysis examples, and show you how you can use it today.
What is Text Analysis?
Text analysis is an umbrella term encompassing AI-empowered techniques that help derive meaningful information from unstructured data. These insights, in turn, help make informed, data-backed decisions, enhance productivity, and improve business intelligence.
Text analysis can:
- help analyze customer preferences, trends, and needs, assisting you in developing better products and features.
- Help study a substantial amount of data in real-time – without occupying your team’s time. Since text analysis with AI reduces manual work, productivity soars.
- Reduce the scope of error - by encompassing Machine Learning and Natural Language Processing (NLP) to unify criteria of analysis.
Common use cases for Text Analysis
- Healthcare: The industry uses text analysis to find patterns in doctors’ reports, identifying patterns in patient data. You can also use it to detect disease outbreaks by discovering cases in social media data.
- Research: Researchers use text analysis with AI to explore pre-existing literature to identify trends and patterns - or categorizing research survey answers by topic or sentiment.
- Product development: By analyzing boatloads of customer reviews and trends, text analysis helps determine in-demand features. By analyzing customer reviews on Amazon, a young analysts team, for instance, studied the price customers were happy to pay for a new market they were tapping into.
- Customer service and experience: By automatically studying various data such as critical tickets, call notes, surveys, and more, businesses can identify urgent requests to respond to and discover sentiment around their product/service.
Text Analysis vs. Text Mining vs. Text Analytics
The world of text and unstructured data is... well, an unstructured one. There are dozens of terms that are often (mis-)used - so let's get some clarity in these.
Text Analysis and Text mining
These terms are commonly used interchangeably - and rightfully so.
If we're getting nitpicky, the roots of text analysis lie in social sciences, while text mining is delved from computer science. In today's context, however, they both refer to obtaining data through various statistical techniques.
Text mining uses techniques like Machine Learning and NLP to pull information about sentiment, urgency, emotion, or topical categories and context out of structured data - essentially to understand human language. Text mining or text analysis techniques could therefore identify customers' sentiment towards your product or brand based on survey responses or feedback forms.
Text mining processes typically include speech tagging, syntactic parsing, named entity recognition, but also more basic techniques for acquiring and processing data - e.g. web scraping and crawling in order to make use of dictionaries and other lexical resources and for processing texts and relating words.
In a nutshell - text analysis is used for qualitative insights - detecting sentiment in language, or topics and context in any free-form text.
Text Analysis vs Text Analytics
The other side of this coin is text analytics that focuses on quantitative insights.
Text analytics draws valuable, recurrent, and emerging patterns, themes, and trends from text-based data - e.g. identifying patterns from data gathered over a year to determine annual trends.
With text analysis, we can identify the sentiment of our customer survey responses. If we wanted to additionally focus on metrics like how many surveys were completed in which timeframe or location, we would opt for a text analytics tool that creates graphs, tables, or reports.
Choosing the correct text analytics technique depends on the dataset available. In most cases, you’ll need to use a combination of two techniques or more to get actionable insights.
Let’s look at a scenario where you are getting hundreds of customer support tickets on a daily basis:
- Text analytics for quantitative results: Let's say you want to analyze how many customer support tickets were solved on any given day, and how fast. You can use text analytics tools to build dashboards to compare performance across different timeframes.
- Text analysis for qualitative insights from unstructured text: Structure customer feedback from various sources like social media, online reviews, chatbot conversations, support tickets, and customer surveys by topic, sentiment, or urgency.
- Text extraction for identifying specifics: Extract the name of the person who sent the support ticket and enter it into a database for future communications.
Challenges of Text Analysis
Any employee who has ever had to review customer support tickets or the company's info@ mailbox will instantly know the benefits of using a text analysis software:
- It is scalable - no matter the volume of incoming text, the software can handle the variance.
- It is consistent - humans make mistakes - that's natural. Or in cases where you work in a team, opinions and criteria for decision-making can differ.
- It analyses text in real-time - analyzing incoming emails at scale can take minutes instead of days and weeks.
There are however some challenges that should not be overlooked:
- The ambiguity of human language - An "apple" can refer to a fruit or the company, and context matters a great deal in distinguishing between the two. One way to mitigate this is to add NLP capabilities to your pipeline.
- Multi-lingual scenarios - most text mining algorithms operate in a specific language making processing multilingual documents inefficient.
- Usability - Text analysis tools are more often than not designed for trained knowledge workers and are typically too complex for the average business professional.
What is Text Analysis Software and how to choose the right one?
Text analysis software or text mining software offers actionable insights from text data using NLP and machine learning.
If you are going for the no-code SaaS route, on your part, you have to integrate the data source into the software, and the platform handles the rest. This includes identifying key phrases, language, themes, and entities, text sentiment analysis, and finding recurrent or fresh patterns.
For instance, a business can use an AI-based text analysis software to increase the bottom line by flagging customers that are suitable targets for cross-selling and upselling. They can also interpret customers’ sentiment around their service and product to improve it. Or, businesses can use the software to pin down faulty behaviors that speak volumes of fraud.
Finding the right fit starts with analyzing your processes: what is the insight you're looking for? Dependent on this answer, you may want to explore different text analysis techniques before you start searching for the right vendor.
Next up, let’s dig into the three main text analysis techniques and see what each does and how they differ.
Also known as text tagging or text categorization, classification involves assigning certain tags to data.
In the most simple terms, data can be recognized and classified in three approaches. These are:
- Content-based classification: In this classification type, the contents of each file are the basis for categorization.
- User-based classification: User-based classification relies on the user’s knowledge of creation, editing, reviewing, or dissemination to label sensitive documents. These individuals can specify how sensitive each document is.
- Context-based classification: Context-based classification focuses on the context of the data, such as the location, application, and creator, as well as other variables that affect the data.
We also wrote a longer piece on classification where you can find some guidelines on how to get started.
Text extraction uses AI to automatically scan text and extract pre-defined keywords and phrases from unstructured data. It could include information from data sources such as news articles, website conversations, surveys, and reports. Think of the find feature in Google docs (Control + F or ⌘ on Mac), but on a massive scale.
Virtually all data extraction is performed for one of three reasons:
- To archive the data for secure long-term storage
- For use within a new context (during domain changes for example)
- In order to prepare it for later stage analysis (the most common reason for extraction)
Text analysis AI service helps determine which words or series of words reoccur by frequency. Referring back to the example of doing a find in Google docs, you’ll see that when you type in a word in a search box, it gives you the frequency of times the term is used in the text.
There are naturally other techniques that work side-by-side with these: collocation, clustering, concordance, and many, many more - but chances are you'll want to use these in combination with one or more disciplines.
How does Levity help with Text Analysis?
Without text analysis software, you’re likely going to deal with overflowing, uncategorized text-based data that takes hours to analyze manually. This means, as a small-medium-sized enterprise, you’re missing out on valuable data, which can help you understand and serve your customers better, build better product features/services, and more.
Here’s how Levity helps with all this and more:
Automatically sort and assign service requests based on location, region, service type, or any other custom category you see fit. This way, you don’t miss out on urgent requests: you can set automatic workflows for commonly asked questions, and ensure topic-expert agents get back to requests.
Here’s how this works: the software studies the tickets’ content (subject and body request) for urgency, language detection, and sentiment analysis to tag requests as urgent, negative, and so on.
You can also create custom models to detect messages based on product, department, or anything else specific to your business.
With an average person seeing an exchange of 121 business emails daily, inbox organization is a must to stay on top of important messages.
Levity uses AI to tag messages based on its content. For example, it can classify messages with the following tags: ‘urgent,’ ‘classified,’ ‘personal,’ or by customers or department.
This is highly valuable for managing your online reputation and responding to customer reviews.
Study the data from customer reviews, survey forms, social media text, and other written sources. Detect the text for its sentiment (positive, negative, or neutral, topic, and intent).
You can also apply a filter for urgency to de-escalate negative brand mentions or reviews quickly. Here’s more on processing & following up on customer feedback with Levity.
Additionally, use sentiment analysis to learn written messages’ tone instantly. It can help you in several ways, such as prioritize angry or negative customer support tickets, track customer response to specific changes, and more.
If you're new to text analysis, we hope that we cleared some things up for you. If there's one takeaway you should have, it's this: text analysis is no longer the playground of software engineers - any business of any size can utilize research and the capabilities that are out there.
Text analysis for unstructured data is a compelling way to unlock actionable insights from your customer data - be it sentiment, topic, context, or... the sky is the limit!
AI-powered text analysis takes the bulk of work to study, organize, categorize, and identify written data’s tone. This helps you boost productivity, improve customer service and satisfaction, understand your target audience better, and a lot more.