Computers and Technology

Advanced Features of Best Text Analysis Software

The best text analysis software offers advanced features to help users mine unstructured data for insights. These include text search, visualization, mixed methods, and statistical tools.

Amazon Comprehend uses NLP and ML to analyze customer emails, support tickets, product reviews, and social media to identify text themes and relationships. It also identifies key phrases, places, people, brands, and events and organizes texts by topic.

Word Cloud

Using text analysis to extract insights from unstructured data is a crucial skill for business professionals. It combines linguistic and statistical techniques to analyze emails, call center transcripts, and social media conversations for business-specific themes.

Despite this, it can be challenging to hone in on the most important points when analyzing large databases or long pages of raw data. Word clouds make this task much more accessible by visually representing your results and highlighting essential words at a glance.

The best text analysis software includes this advanced feature that helps you understand your data in a new way and easily communicate it with the rest of your team. These visualizations are pre-built and customizable, allowing you to use them for almost any purpose – from a quick overview to more elaborate data explorations.

You can create a word cloud from an existing data source, such as your documents in MAXQDA, or upload your files. You can adjust the layout and color scheme to match your presentation style.

Another helpful feature is the ability to filter out words that aren’t relevant to your data. These include articles (a, an, the) and familiar but not meaningful words like customer service or user experience.

DiscoverText combines flexible software algorithms with human-based coding to enable accurate and reliable large-scale analyses. It uses topic detection and entity extraction to deliver actionable insights for various business applications, including marketing, analytics, and operations.

Topic Modeling

If you’re a business or organization that receives large amounts of text from customers, it can take time to make sense of it. This is where text mining tools come in handy. They can automatically analyze and interpret unstructured data from multiple sources to help you understand customer feedback and take action.

One of the most advanced features of best text analysis software is Topic Modeling, a machine learning algorithm that groups the feedback into broader concepts. It analyzes word frequency patterns, order, and distance in texts.

Unlike word frequency and dictionary-based techniques, automatic topic modeling doesn’t require pre-defined training. This means that you can analyze large volumes of data in minutes and get results that you can use.

As a result, you can quickly identify common themes and underlying issues in textual feedback. For example, if your website gets a lot of customer feedback about pricing, you can group it with other similar comments.

It’s a helpful tool for detecting urgent feedback that must be dealt with immediately. It also helps customer support teams prioritize tickets that need immediate attention. Using this type of analysis can eliminate manual tasks and speed up processes so that you can deliver a great customer experience every time.

Pre-built Models

Pre-built models are an advanced feature of best text analysis software. They help you add intelligence to your apps and flows without building, training, and publishing your models. They also allow you to implement AI in your workflows quickly, boost employee productivity, and improve customer experience.

In addition, these pre-built models often include the ability to combine different data sources into one database. This makes it easier to see the whole picture of what customers say about your brand.

Another advanced feature of text analysis software is the ability to visualize processed data. This allows you to explore relationships between terms and create interactive diagrams.

These features are advantageous when using text analysis to analyze customer feedback. Still, they can also be used in other business areas, such as identifying fraud, detecting regulatory non-compliance, and screening job applications.

The best text analysis software is designed to make it easy for you to analyze text from multiple sources and integrate those insights into your business. This means you can get a complete picture of what your customers are saying across all touchpoints and channels, so you can make better decisions about how to improve their experiences.

In addition, many good text analysis tools use machine learning algorithms to help you develop topic models. While this is a valuable tool to establish topics for VOC programs or Employee Experience programs, you may need help identifying trends and sentiment in your data for action.


Today, we have text everywhere – support tickets, emails, work chats, and social media. This unstructured data is a treasure trove of information that can be used to make decisions and develop better products and services.

Having text analysis software that is flexible and easy to use makes it easier for organizations to find the right insights quickly. There are many SaaS solutions out there that are designed to be scalable, inexpensive, and easy to implement in just minutes.

Another important factor is how well the text analytics tool will fit into your workflows and integration needs. Some tools integrate directly into your existing processes, while others offer an API so you can easily export and import data between them.

Some text analysis platforms are explicitly designed for specific use cases based on the industry and technology you are in. Companies that handle sensitive customer data or medical research may want to consider a text analysis platform with specific NLP capabilities like broad entity recognition or sentiment analysis.

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