The Top AI Text Analysis Tools You Should Be Using

With 175 Zettabytes of data predicted to be generated by 2025, AI text analytics is a game-changer. AI text analysis tools help turn this overwhelming data into valuable insights.

And these enable us to make smarter decisions and spark innovation. Virtually any business or organization dealing with large volumes of text data can benefit from text analysis software.

It's a great way for marketing pros to understand your customers better, craft personalized campaigns, and build stronger brands. Take the example of a new product launch - AI text analytics can analyze customer reactions in real-time.

AI text analytics is also transforming healthcare by simplifying patient data management. This means quicker diagnoses and personalized treatments.

Finance is another winner. AI can catch fraud, automate customer service, and analyze market trends. Sentiment analysis of financial news can even predict stock market moves.

With AI developing at breakneck speed and new products emerging all the time, choosing the tool that best matches your needs can be tricky. In this article, we’ll compare the top AI text analysis tools and the unique benefits they offer.

What is AI Text Analysis?

AI text analysis is the process of extracting useful information from large amounts of textual data using artificial intelligence. Examples of this information include the sentiment of a text (sentiment analysis), identifying patterns (thematic analysis), language detection, and creating summaries.

These signals in unstructured feedback are highly valuable to businesses. For example, analyzing the signals in online reviews and support tickets helps customer success teams identify and understand the issues that make customers very unhappy.

These results can be combined with other data variables like customer tenure, call length, customer plan, and so on. This combined analysis is where businesses get the most value.

Text analysis used to be done manually, but these processes are slow and prone to human error. Now we can use AI tools to automate the analysis and increase speed, capacity, and accuracy.

The first step is obtaining data from appropriate sources. For example, external data might be collected from review sites like Trustpilot or internal data from customer support tickets. The next step is to turn the human language into data that computers can understand using Natural Language Processing (NLP) methods.

Text Analysis using NLP and ML

Modern approaches to Natural Language Processing (NLP) use complex algorithms and machine learning (ML) to process text feedback and construct a model that makes sense of qualitative data. The models extract specific aspects, such as phrases, themes, sentiment, and questions.

Generative AI

Generative AI, which has a really good understanding of language, now plays a crucial role in cutting-edge approaches to text analysis. The technology understands the feedback context and subtleties of human language. It can analyze feedback with high precision.

This also means businesses can now analyze a wider variety of text feedback faster and deliver the output as a summary rather than a model.

Large Language Models (LLMs) and Text Analysis

Large Language Models (LLMs) also play a crucial role in AI text analytics. They are trained on vast amounts of relevant text data. This training data enables them to understand the context, nuances, and subtleties of human language.

This deep understanding allows for more accurate text analysis, whether it’s creating summaries, sentiment analysis, or understanding the main themes in a dataset.

What should you consider when choosing AI Text Analysis software?

The best way to select the right text analytics software for your organization is to start by considering what you want to do with it. Let’s take a look at some common uses for text analysis and assess the most important software features for each scenario.

Analyzing live chats

Text analysis software is a great way to get actionable insights into how your customers feel at any given moment. If you want to analyze chat questions from a chatbot, for example, customization and flexibility are really important. The ideal text analysis solution will allow for human-in-the-loop processes, where humans and AI collaborate to refine and improve results.

Analyzing text verbatims

Text verbatims are great for giving you deeper insights into the customer experience. If this is your aim, you’ll need software with greater capabilities. You will need text analytics tools that are scaleable, accurate, and flexible.

Pay close attention to the type of model being used. Some text analysis software use rule-based models which have been designed for specific industries and verticals. These text analysis models rely on predefined keywords and they may miss unexpected themes in your unstructured data.

More advanced solutions use AI and machine learning to discover new themes as more data is added. You should also ask what training data and text analysis methods are being used.

Sharing insights across organizations

Getting insights to those who need them can be critical for both business development and fixing issues fast. Technical and non-technical teams should be able to use text analytics tools effectively and efficiently. Clear and accessible visualization tools make it easier to share qualitative insights with all stakeholders.

The Top AI Text Analysis Software

XM Discover

Qualtrics is a long-established and prestigious name in the customer experience management or XM industry. XM Discover is software that Qualtrics purchased from Clarabridge. So ideally you need to be gathering data with Qualtrics in order to use Discover XM.

Qualtrics also have TextIQ. This is a much more basic AI text analytics option for those who don't need to drive decisions from the analysis.

Getting started with XM Discover

To get started, you select a specific industry AI model and then begin training it to customize it to your business and market context.

The training involves creating rules for the AI to apply to text analysis. It typically takes anything from 4 weeks to 6 months, depending on the market and product complexity.

Analyzing your data with XM Discover

Once the AI rules are complete, XM Discover quickly analyzes the data. Your team will spend several months learning to use the platform and how to analyze the data to discover insights. Your team will need the expertise to use the platform or outsource this to professionals.

XM Discover allows you to create custom calculations to fit your business reporting needs. A caution here is that the platform is built to be standardized. Custom aspects often don't work as needed.

XM Discover promises to deliver new and improved features soon, but the market is wary of how long it typically takes Qualtics to ship product improvements.

XM Discover pricing model

The starter pricing for XM Discover is competitive, but plans become cost-inefficient as you add more data.

XM Discover G2 reviews

“Our team did not have the expertise to fully utilize the features with this product.”

“A little bit of a learning curve to understand setting up and configure.”

“Requires a lot of admin time for set-up, onboarding, and maintenance.”

Thematic

Thematic started out as an NLP consultancy helping companies understand their unstructured text data. From those early beginnings, the company is now a leader in feedback analytics.

Thematic’s proprietary AI automatically identifies themes within text data. It uses cutting-edge machine learning models and Generative AI to continuously improve accuracy and adapt to evolving language patterns.

Getting started with Thematic

Setting up Thematic is a straightforward process. You can be up and running in a matter of hours. Your unstructured data is transformed into themes and categories automatically by Thematic’s AI. This means you don’t need to spend valuable time and effort manually setting up rules.

Analyzing your data with Thematic

Once you have your results you can easily refine or remove themes yourself to better align insights with your specific business goals. This human touch allows for greater accuracy and relevance.

Features like Thematic Answers use Generative AI to ensure the software is accessible to all team members. All you need to do is type in your question. In seconds you'll get a summary answer with verbatims and powerful visualizations.

When more data is added, Thematic’s AI automatically discovers new themes for you. This is a big advantage over other models where new themes need to be added manually.

Thematic pricing model

Thematic’s pricing is competitive and offers a tiered system depending on the size and needs of your business. Plans become more cost-efficient as you add more data.

Thematic G2 reviews

“It does an excellent job using a single view to break down the verbatims into themes displayed by volume, sentiment, and impact on our beacon metric, often but not exclusively NPS.”

“Their AI model does a great job to jump-start your categorization model with the ability to merge, tweak, add new themes quite easily.”

“It takes time to think through the best way to represent your data and its not something to just casually attempt.”

unitQ

unit Q is a text analysis software solution to measure the gap between customer experience and expectations based on review scores and comments.

unitQ focuses on delivering a unique feature called the unitQ score. This quantifies the quality of your product or service.

The AI-generated score indicates the percentage of users reporting a seamless experience, based on the keywords that arise in feedback from reviews, social media, and CRMs. Their integrations are mainly limited to these data channels.

Getting started with unitQ

UnitQ begins by manually analyzing an organization's feedback datasets, to build the AI rules that determine the keywords or labels for the organization. The time to train and build the bespoke model depends on the complexity of the business and the nature of the feedback.

From this process, they train an AI model specific to your organization to categorize feedback into keywords and extract the unitQ score.

Analyzing your data with unitQ

Once the rules are complete, it's quick to analyze the data. As new products are released or updated, the AI needs to be retrained to learn the new context. This is necessary because AI uses a supervised approach to analyzing text. It cannot detect new issues without being trained.

Your product and operations teams will most benefit from learning to use the platform. Automated alerts make it easy to quickly make operations and engineering teams aware of emerging quality issues for new features or upgrades

If you are seeking strategic insights, or want to delve into the feedback for a rich understanding, unitQ will likely fall short.

unitQ pricing model

unitQ’s pricing plans are not standardized since the work needed to train a bespoke AI model for each customer is unique.

unitQ G2 reviews

“Real-time insights and alerts help us catch user quality issues quickly.”

“It takes some time to understand and use all the features in the best possible way.”

“The process of creating and managing monitors could be more transparent, as well as manual tweaking of monitors and historical aggregation of feedback by monitor (i.e. by topic).”

Medallia

Medallia is an all-in-one customer experience management platform with a text analytics tool.

They have a strong partnership with analyst companies and are profiled extensively by Forrester.

Getting started with Medallia

To get started, you need to build a full and comprehensive list of rules the AI should follow to categorize text data into topics. Typically 2 to 4 people from your company will work with a Medallia specialist to create rules. This takes anything from 4 weeks to 6 months depending on the market and product complexity. Alternatively you can use their pre-defined taxonomies for certain industries like hotels, airlines, and banks.

Medallia uses these rules to build machine learning models to analyze your data for themes, intent, specific emotions, sentiment and effort. From this analysis you can get high-level views of sentiment, emotions and topics in feedback.

Analyzing your data with Medallia

Your team will spend several months learning to use the platform and how to analyze the data to discover insights. Alternatively, this can be outsourced to specialists.

Medallia's rule-based approach works well for companies in industries where issues are likely to stay the same, but it’s time-consuming to set up and maintain.

Medallia pricing model

The text analytics software is available for Medallia customers. Costs are added for rule building and data processing. In other words, the cost arises from the manual work used to build the customized model. You will incur ongoing costs to maintain the rules and any datasets, and professional services must be purchased through Medallia or one of their partners.

Medallia G2 reviews

“The integration with other programs is simple and straightforward forward, particularly with the help of our Medallia Professional Services team.”

“The dependency on third-party to configure anything in the platform is challenging.

“We’d love to see better analysis of unstructured data, leveraging AI and advanced text analytics tools. We still struggle to tell stories using their TA topics.”

Chattermill

Chattermill is designed to unify customer feedback data from many channels and integrate the analysis with your metadata for advanced analytics.  Based in the UK, they mainly focus on the retail and travel sector.

Getting started with Chattermill

You start by training a bespoke AI model, customizing Chattermill’s AI to your specific business intelligence needs. Chattermill recently white-labeled their AI as Lyra AI technology.

Lyra uses a combination of NLP algorithms and Generative AI to categorize text feedback into sentiment, phrases, and topic clusters. Their ASBA or aspect-based sentiment analysis delivers a sentiment score for topic clusters. This is a method invented in the early-2000s for fine-grained opinion mining.

The training time varies depending on the market and product complexity, and uses a combination of supervised and unsupervised AI models.

Analyzing your data with Chattermill

Once your bespoke model is built, it quickly analyzes the data. Chattermill is known for its attractive user interface and visualizations. You can create custom dashboards and set up analysis automations to fit your business reporting and CEM needs.

To help even non-technical teams use the tool and hone their skills, Chattermill recently developed an extensive library of training courses to enhance their customer experience management capabilities.

Chattermill pricing model

Chattermill's approach provides for both strategic and tactical insights, making it useful for several decision-makers. Their pricing is competitive. Plans become more cost-efficient as you add more data.

Chattermill G2 reviews

“The platform is user-friendly, super easy to navigate, even for those without extensive data analysis experience.”

“Lack of report personalization can sometimes be an issue, even though the tool is easy to use and does report fast.”

“Unfortunately, it's currently not so easy to connect various data sources to Chattermill or to upload and enrich the Chattermill data with further data that might be available within the own company.”

Comparing The Top Text Analysis Software

Key Takeaways

Thanks to AI and machine learning, text analysis techniques can now be applied to enormous unstructured text data to generate accurate and actionable insights in real-time.

It is easier than ever before for organizations to tease out specific issues with product launches or determine the key factors impacting their NPS.

Choose the tool that's right for your organization

There are so many great text analysis tools on the market right now.

XM Discover is a super option for larger organizations already using Qualtrics products, while Chattermill’s  simpler approach is good for non-technical teams.

Product teams should take a closer look at unitQ which has been specifically designed to monitor product quality and issue anomaly alerts for faster response times. While Medallia is better suited to organizations like hotel and restaurant chains that expect consistent, predictable feedback.

And, finally, Thematic is great for analysts and insights specialists who need to build alignment across teams and organizations. Thematic puts you in the driving seat when it comes to validating accuracy and being in control, while still benefitting from automation with the latest Large Language Models (LLMs).

The Future of AI Text Analysis

Text analysis used to be a slow, clunky manual process that would take hours. Now non-technical teams can access actionable insights in seconds. As Natural Language Processing, Machine Learning, and LLMs become ever more advanced, text analysis software will transform every industry. It’s the beginning of a more agile, customer-focused future.

It Suits You!

We think all next-generation organizations should be using AI text analytics. And with so many options you should pick the one that suits your unique requirements. The best way to do that is to try them out for yourself.

Why not start right here with a free guided demo of Thematic using your own data?