What is Text Analytics?
At the most basic level, text analytics is a technology focusing on deriving insights from verbatim comments. The free-form text goes through a process that mines it for meaning, translates it for the system, and then processes it for insights. It’s capable of discovering significant patterns within this text data, which is used for understanding what people are feeling in their responses, how often topics come up, and the context of the text.
For software to understand what people are saying in unstructured text, it needs to go through a structuring process that identifies key pieces of information, categorizes the information, and allows it to be interacted with to find patterns and other meanings. This translation allows the systems to discover the insights that are most important to your organization.
Since you’re frequently dealing with large volumes of data when you’re working with verbatim comments, the text analysis process needs to be as automated as possible for both data collection and analysis.
What is Text Analytic Software?
Text analytic software is a type of tool that performs text analysis. This term is often used interchangeably for several types of systems that work with text data in unstructured form. Deciding on the right type of analytics software for your organization depends on your overall goals for your text data, the resources you have available for implementation, and your existing systems.
- Text mining and text coding: This tool category focuses on processing the verbatim comments in your data sets, allowing you to categorize this information, sort it into different topics, and add labels to it.
- Text analytics: This software focuses on providing your organization with insights on the verbatim comments you have available. You can interact with your data to look for patterns, find common themes, and learn more about the sentiment behind each comment.
- Text data visualizations: After you have your data coded and analyzed, data visualization tools allow you to present your findings in easily understandable forms. This type of solution is excellent for presentations, reports, and similar functions.
- Custom-built tools: In some cases, you may have specialized requirements and use cases for text analysis that is not available through commercially available software. In these situations, custom-built tools using barebones APIs allow you to create exactly what you need to support your text analytic projects. However, this option is resource-intensive and requires an experienced development team and other specialists.
- All-in-one verbatim analysis software: You can get a complete platform that delivers all of the tools needed for your text analytics needs. These solutions combine text analytics, data visualizations, text mining, and text coding into one convenient platform. In most cases, this is the right choice for your organization.
The Benefits of Text Analysis
In the modern business world, you’re going to lose out if you don’t have text analysis to help you improve the experiences of your customers, employees, and other partners. Moving to a competitor is all too easy in many industries, but text analysis can give you the edge you need to respond to changes in the market, meet the expectations of everyone you’re working with, and create sustainable growth for your organization.
Here are some of the many benefits you can gain when you implement text analytic software:
- Gaining value from unstructured data: The sheer volume of feedback data available today is almost overwhelming, but it’s useless unless you can turn it into something that a computer can understand. Text analysis simplifies this process and makes it possible to work with some of the most valuable data you’ll ever have access to.
- Understand the experiences of customers and other key players: You can’t improve an experience until you truly understand what’s going on in the heads of the participants. The verbatim feedback gives you valuable insights into this process, at a scale that gives you information you can truly act on.
- Drive repeat customers: Another benefit of learning more about customer experiences is that you can increase loyalty by continually improving the interactions that everyone has. When you can build up a happy customer base, you increase your revenue and gain many other benefits.
- Gain more data for strategic decision-making: Data-driven decision-making is an important part of growing your business, as you can combine your experience with hard data to understand whether you’re making the right decisions. The more data you have available, the better.
- Discover what’s truly important for your experiences: You may be focused on the wrong areas in the experience, when customers, employees, and others may have other expectations. Align your investments with these expectations so you can make the most of your resources, while giving everyone what they’re looking for.
- Improve productivity: Manually working with unstructured data takes a lot of time, and is not realistic when considering the scale of text data available for many organizations. While manual processes may work at first, especially when you’re smaller, you’ll end up with insights slipping through the cracks, inconsistent processes that lead to errors, and other issues that make it difficult to scale. Text analytics tools make it more efficient and productive to work with this information.
- Surface new opportunities: You may not realize that there are new markets or product use cases just waiting for you until you start looking at your verbatim comments. This feedback can help you find new ways to grow your business and improve your products and services.
How Does Text Analysis Software Work
The exact process for text analysis depends on the type of solution you choose and the text that you’re working with, but there are a few common steps that this information goes through before you can start using it to make business decisions.
The first part of the process requires you to collect verbatim comments. This data collection process can involve many types of sources, since you can work with unstructured text data. Everything from social media comments to survey responses is fair game.
Once this data is collected, it needs to be mined and coded. This step prepares it for text analysis. The software will look at each comment, break down the meaning of the sentences, categorize it, sort it into different topics, and otherwise categorize this information.
From there, it’s ready for analysis. You can dive into the data to learn about important topics, the trends showing up in this information, and other key insights that can help your business grow. You choose different types of learning models for the system to effectively process this information, using advanced technology such as Natural Language Processing (NLP).
These insights can be transformed into data visualization, sent into other software for other types of analysis, and help in many areas of your organization. The vast majority of this entire process is automated, making it possible to scale text analysis.
What is Text Analysis Software Used For
Some of the most common use cases for text analytics software include:
- Voice of the Customer programs: Customers provide plenty of feedback, and text analysis software makes it easy to learn more about what they want out of your organization.
- Find growing problems: If many customers are running into issues with your products and services, you may not realize the sheer scale of the issue. Text analysis can show you the trends that indicate areas you need to fix.
- Enrich data from other sources: A commonly used metric for gauging customer satisfaction is a Net Promoter Score survey, but the insights you get from this approach make it difficult to understand the exact factors that influence this score. By using text analysis software to look at the open-ended feedback submitted alongside the survey, you can better understand why customers pick the responses that they do.
- Evaluating new products and services: Understand how customers respond to new products and services to determine whether you’re going in the right direction.
Common Text Analysis Tool Features
Each text analytics tool has its own range of capabilities, but some of the features that you might end up seeing in your selected software includes:
- Customized rulesets: You can create analysis rulesets that are customized to each use case that you’re working with. That way, you can focus on the exact type of analysis that is best suited for surfacing the insights that are most important for your business goals.
- Automatic translation: You don’t need to drop data from your verbatim comments simply because it’s not in your country’s native language. Text analysis tools often include automatic translation, which allows you to tap into these data sets as well.
- Convenient APIs: If you want to expand on the capabilities of text analysis tools or integrate them with other technology that your company uses, you can leverage these APIs to make it happen.
- Importing and exporting data between software: Easily move your data into and out of text analytics software.
- Developing dashboards: Convenient dashboards give you an at-a-glance look at text analytic insights. People in leadership positions can use these dashboards for strategic decision-making, or to get a big-picture view of business operations.
- Analyzing all text data: Both structured and unstructured data can be combined in many text analysis software, expanding the sources that you can work with.
- Real-time text analysis: Some solutions let you see insights in real-time, such as looking at trends in social media comments or customer support tickets.
Choosing Text Analysis Software
Picking the text analysis software that makes the most sense for your organization is based on many factors. When you’re evaluating this type of software, look at the capabilities, the type of data you work with most often, and what you need to get the most out of this information. By aligning your text analysis software needs with your business goals, you can set your company up for success.
If possible, try to go through trials and demos with a proof of concept that uses real-world text data. That way, you can see whether you are getting the right insights to meet your decision-making priorities, or if you need to reconsider the software capabilities that you’re looking at.
Getting the Most Out of Text Analysis Software
When you decide on text analysis software for your organization, make sure that you’re getting the most out of your investment. Identify key areas that could use the help of text analysis, such as your customer-facing programs. Look at your business goals and identify open-ended comments that could help you make better decisions in these areas. Consult with key stakeholders to determine what they want to get out of text analysis software, and involve them during the evaluation process to get buy-in for your selection.
During the implementation process, make sure that you have the right training resources so that employees know how to use the software, what types of insights they can get from it, and how the software makes it easier to arrive at these insights.
Collecting Data for Text Analysis Software
You have more open-ended data for text analysis software than you might think. Consider how many places that people can place comments or talk about the experiences they have with your company. Internally, you have order processing systems, customer relationship management software, customer support tools, marketing platforms, and sales tools that all contain significant data sets already.
Externally, social media is one of the most valuable sources for open-ended comments, although you can also discover more data on review sites, blogs, and other web pages. By bringing these data collections together into your text analysis software, you get a comprehensive view of all relevant feedback.
One term that you may encounter frequently when you’re looking at text analysis software is sentiment analysis . As this term implies, you can look at what a respondent is feeling in that comment. These emotions can be quite important for understanding what people mean in their comments, as there’s a lot of nuance that can completely change the meaning of text.
With sentiment analysis, the text analysis process moves beyond simply categorizing the text or providing a relatively literal understanding of the meaning. Instead, it goes deeper into this data to discover these emotions.
Another common term in text analysis is wordspotting, which is also sometimes called keyword spotting. This happens early in the text analysis process, during text mining and text coding. The software looks for how many instances of words and phrases occur in this data, and can identify important keywords that frequently occur.
You can also define important keywords through custom rulesets, which allows you to sort through the data for this priority information.
Text categorization happens early on in the text analysis process, and allows you to group comments into different categories. That way, you can see some of the most common trends in your data that come up.
These categories can show you what the priorities are among your customers, discover problem areas that need to be addressed, and show you what people are talking about frequently.
One way that text analysis software can categorize the text is through topic modeling. Rather than just looking for specific keywords, the software looks for an overall group of words that are related to the topic. Since verbatim comments can convey the same category through many different phrases, being able to let the software model topics and look for these groups can help you bring all of the relevant data together.
Text Analysis Compliance
Data privacy regulations and laws frequently govern what you can and can’t do with certain types of data. If you want to leverage your verbatim data sets through text analysis tools, you need to keep it compliant so your organization doesn’t incur any penalties.
For example, personally identifiable information is not needed to get the insights you need to make decisions in text analysis, since you’re looking at the overall data rather than one specific response. You can remove personally identifiable information in the data sets through the text analysis tool so you remain compliant.
Limits to Accuracy in Text Analysis
Natural Language Processing is an amazing technology, but human speech is incredibly complex and changes constantly. Text analysis software is not able to accurately analyze every single piece of feedback that concerns your organization, but it doesn’t need to to be useful.
Since you’re evaluating large data sets at scale, text analysis is able to deliver insights based on overarching trends and patterns within this data. If the system doesn’t quite pick up on the right connotation in a few individual responses, it doesn’t end up ruining the insights or compromising the data quality that is delivered.
Machine Learning for Topic Modeling
Machine learning, a type of artificial intelligence technology, is incredibly useful for topic modeling. Machine learning teaches the computer about the text that is relevant to topics, helps it learn how to identify topics, and guides the system in the modeling process. Without machine learning, which allows the system to continually learn from the data that is fed into the system, it would be impossible to handle text analysis at scale.
Natural Language Processing
Natural Language Processing is one of the most important parts of text analysis, as it allows computers to make sense of verbatim comments. Since your data sources for text feedback are typically unstructured, outside of multiple-choice surveys and similar sources, Natural Language Processing acts as that critical translation layer.
You can allow your customers to convey information as though they were speaking, and your text analysis software can work with that as-is. You end up having a lot more flexibility with this approach, which allows you to harness data sets that would otherwise be unavailable to you.
Implement Text Analysis Now
Starting with text analysis is simple when your organization works with Ascribe’s Verbatim Analytics Platform. Get powerful coding, analysis, and visualization tools to get the most out of your unstructured text today.
- What is text analysis? Text analysis is the process of gaining key insights from text data, such as social media posts, survey responses, and comments.
- How do you do text analysis? Text analysis software is a specialized tool that takes unstructured text data, codes it, analyzes it, and then presents the insights in easily understandable forms.
- Why do we need text analytics? Text analytics is essential for truly understanding the thoughts, feelings, and expectations of customers, employees, and other partners in your business. Without text analytics, your organization would not be able to use large datasets of verbatim comments in analysis, as computers need this type of software to learn what people are saying in unstructured text.