Whether it's social media comments, online reviews, or survey feedback, researchers need to find themes and take action. AI text analysis tools make turning text into strategic insights faster and easier. But in a crowded market, selecting the right tool for your needs can be difficult. Here, we look at some of the different options in the market and explain why they may or may not work for your next text analysis project.
What is AI-Powered Text Analysis?
Before looking at the list of best tools, it's important to define... what exactly is AI text analysis? Text analysis (sometimes referred to as text mining) is the process of studying unstructured text data to gather insights. AI text analysis, therefore, uses artificial intelligence to extract insights from text, including patterns, sentiment, key themes, and more.
With these AI-powered capabilities, users can identify key patterns, text sentiment, and emerging themes across various mediums, such as social media posts or customer feedback.
The process of automating aspects of text analysis, such as categorization and entity analysis, has existed for some time, although accuracy issues have limited adoption. Recent advances in Natural Language Processing (NLP) and Generative AI have significantly improved the quality of AI text analysis and made it a viable option for researchers dealing with large volumes of data.
Text Analytics Use Cases
Text analytics use cases vary depending on your industry and the type of data you are analyzing. In market research, for example, text analysis will primarily focus on survey responses, customer feedback, social media comments, and product reviews to identify trends and pinpoint customer sentiment.
On the other hand, AI text analysis is also valuable for businesses and human resources teams looking to gauge the mood of their staff. Employee engagement surveys, for example, provide useful data for management, so having powerful text analysis tools can help improve engagement and efficiency. In healthcare, text analytics assists in processing clinical notes and research papers, enabling faster insights for better patient care and decision-making.
Text analytics is also emerging as a valuable tool in financial services, where it is used to process documents and find relationships between significant economic events.
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Top AI Text Analysis Tools for 2025
Now that we've covered the fundamentals of AI text analysis and its various use cases, let's look at some of the top tools on the market. Each solution offers unique features and capabilities, so it’s important to consider what's going to be most appropriate for your use case and choose one that best suits your specific needs.
1. Displayr
Displayr aims to make text analytics easy with its no-code capabilities and intuitive interface. It utilizes advanced Natural Language Processing (NLP) to understand the context and meaning of text and categorizes verbatims into dynamic themes - in any language. In terms of AI, users can utilize prompts to give precise instructions or ask questions, ensuring anyone can perform text analysis regardless of their experience. Designed by market researchers for market researchers, Displayr excels in analyzing survey data and any other market research-related activity.
Best For: Market researchers or anyone else looking for an easy-to-use, no-code solution for survey and customer data analysis.
Categorize text easily in Displayr.
2. Amazon Comprehend
AWS's Amazon Comprehend is a natural language processing service that utilizes machine learning to extract meaning and insights from text. Some of its typical applications include mining business and call center interactions, managing legal briefs, and classifying and extracting entities from financial services documents to find relationships between financial events.
Best For: Enterprises and developers who need a scalable solution for extracting insights from large volumes of unstructured text, particularly in legal, financial, and customer service contexts.
3. Forsta
Forsta's text analytics software solution offers AI-powered and custom rule-based text categorization. Additionally, it has 12-emotion segmentation as part of its granular sentiment analysis offering. It allows researchers to consolidate different inputs into one channel, making it a good option for anyone looking to analyze multiple data sources.
Best For: Market researchers and CX professionals looking for advanced sentiment analysis and multi-source data aggregation.
4. Azure AI Language
Microsoft's Azure AI Language is a cloud-based service that utilizes NLP to help users analyze text. Available features include Named Entity Recognition, personally identifying and health information detection, language detection, sentiment analysis and opinion mining, summarization, key phrase extraction, entity linking, customer text classification, and more. It is easy to integrate if you already use other Microsoft services or products.
Best For: Enterprises already using Microsoft Azure and those looking for a scalable, cloud-based text analysis solution that can be used for data from multiple industries.
Make sense of text data with the right tools.
5. Blix
Blix offers several valuable features as part of its text analysis offering, such as automated topic discovery, multiple-language support, AI-powered coding, and automated reports and summaries. It is a useful option for market researchers, consumer insights and CX professionals, and CI analysts.
Best For: Market researchers, consumer insights teams, and CX professionals who need an efficient, automated solution for analyzing customer feedback and survey responses.
6. Converseon.AI
Converseon.AI is a conversation-based decision intelligence tool. It combines real-time conversations and social data streams using NLP, advanced analytics and applied AI to provide businesses with predictive guidance. It is a good text analysis tool for anyone looking at managing brand reputation, brand relevance, ESG efforts and customer experience.
Best For: Brands focused on reputation management, customer experience, and predictive analytics, especially those with an active presence on social media.
7. Google Cloud Natural AI
Google Cloud Natural Language AI uses the tech giant's machine learning to help users derive insights from unstructured text. Users can use entity analysis to find and label fields within a document (such as emails, chat, and social media) and then perform sentiment analysis. It also extends to speech-to-text, meaning users can analyze audio and content from across media.
Best For: Companies already using Google Cloud who need a flexible, powerful NLP tool for a variety of text analytics tasks.
8. codedit
Codeit is designed to combine AI with human coding to improve speed and accuracy. Features include theme extraction, AI Codeframe Builder, AI-assisted brand coding, autocoding based on human examples, and verification tools. Codeit's human-led AI approach means it's a great choice for those who want to oversee AI text analysis closely.
Best For: Teams that need a balance of automation and human oversight in text analysis, especially in brand monitoring and market research.
9. nuance
Nuance codes text files, handwritten text, audio-recorded comments and images, allowing researchers to extract and quantify meaning. It also offers multilingual verbatim coding. In terms of text analytics, nuance creates a summary of topics and themes extracted and can also perfrom sentiment analysis. Nuance is best for those in need of multilanguage coding.
Best For: Organizations needing to analyze diverse data types (e.g., audio, handwritten text) in addition to standard text, especially in multilingual contexts.
10. Canvs AI
Canvs AI provides emotion measurement, topical & thematic analysis and a generative AI co-pilot to support users. Users can add, remove or reclassify topics emotions or codes to ensure a more tailored experience. It is a useful tool for anyone conducting brand studies, consumer behavior research, or concept tests.
Best For: Researchers focused on consumer behavior, brand studies, and concept testing who need in-depth emotional insights.
11. Chattermill
Chattermill is an AI customer feedback analytics platform that uses machine learning to analyze and understand customer feedback. It allows companies to unify customer feedback across support tickets, conversations, and social media. The platform also offers detailed reports and visualizations, making it easier for decision-makers to interpret and act on the information.
Best For: Businesses looking to centralize and analyze customer feedback from multiple channels in a single platform.
Sentiment is a common text analysis technique.
Bonus tool: ChatGPT
It is hard to compare ChatGPT with the tools on the list above. The options mentioned above are all dedicated text analytics platforms, while ChatGPT is essentially an AI-powered chatbot. However, ChatGPT can perform basic text analysis. Functions like sentiment analysis or entity recognition can all be quickly done in ChatGPT, required you provide the right prompts.
There are obvious limitations around formatting and token limits, but given the free version is very effective, ChatGPT can be a good option if you have a limited budget and a small data set.
What makes a good AI-powered text analytics tool
Several factors and capabilities will make an AI-powered text analysis tool valuable to you and your business. Accuracy is a top priority for market researchers—or anybody else who needs to deliver valuable insights. This means ensuring tools can precisely interpret and analyze text and extract meaningful insights such as sentiment, themes, and key phrases with minimal error.
Another factor is the ease of use and ease of implementation. If you are a time-poor researcher looking to analyze the sentiment of a recent customer survey, you don't want to spend hours setting up your text analysis tool and integrating it with your other platforms. You want something that will work and deliver results as soon as possible. This convenience has led to the rise of no-code tools, such as Displayr, in the market.
Choosing the right text analysis software
When it comes time to pick a text analysis tool for your business, there are some of the key takeaways to consider:
- AI-powered text analysis tools are essential for extracting valuable insights from large volumes of unstructured data.
- Accuracy, ease of use, and customization are crucial factors to consider when choosing the right tool.
- The ideal tool depends on your industry and specific use case—whether it's market research, HR, healthcare, or financial services.
- Look for no-code, intuitive solutions like Displayr if you're short on time or experience.
Although this list only scratches the surface of text analysis tools available, it should highlight that each tool is better suited for specific use cases. Beyond individual features, it is important to choose a partner with a proven track record in the field of AI-powered text analytics. Try Displayr's text analysis tool free.
