10 features to look for in a text analytics platform

Apr 20, 2022 | Text analytics

AI-based analytics platforms have demonstrated how versatile they can be and how quickly they can optimize any set of data. They are absolutely worth investing in for any organization with large sets of unstructured data. However, with all the text analytics platforms on the market, it can be hard to know which one best adapts to the needs of your organization. 

The feature checklist we provide in this article will narrow the selection down to only a few robust platforms worth further evaluating. Depending on how advanced your data analysis needs are, and the available budget, you can be confident that these features provide you with the knowledge required to find the right solution for your organization.  

10 features to look for in a text analytics platform

These are the most important features to look for in a text analytics platform:

1. Multichannel data sourcing

The best text analytic platforms can handle data from both public and private sources. This includes survey data, social media content, blogs, support tickets, emails, complex data sets, and internal documents.

2. Multilingual processing

Native proficiency in several languages guarantees that a text analytics platform can process data with no information lost in translation, thereby providing the highest level of accuracy. While many text analytics platforms only possess the capability to process data in one language, state-of-the-art text analytics software can process several languages with native proficiency.

3. Sentiment analysis (can the AI tool be trained for your specific data?)

Top-tier analytic platforms can identify positive, negative, and neutral ideas within a body of text. The most advanced AI tools can be trained to change their sentiment evaluation, operating under a user’s particular preferences. Moreover, visualization tools can be used to produce results that better align with an organization’s messaging objectives. 

4. Tracking sentiment evolution over time

User sentiment is likely to evolve, especially during the development of an event such as a product rollout. Powerful text analytics tools can track and evaluate communications constantly, from the beginning of a marketing campaign to after its conclusion. Furthermore, the best sentiment analysis software can produce immediate results, letting businesses gauge the success of their strategies in near real-time. 

5. Extraction of multi-word expressions

Context matters. Human communication is full of idiomatic expressions and words that change their meaning depending on the sentence they are used in, which is why a text analytics solution must be able to extract fully expressed ideas rather than single-word keywords. This is the difference between a text analytics software that extracts the single keyword “problem” versus the multi-word expression “problem shipping to Canada”.

6. Advanced taxonomy management

Taxonomy management gives users the tools to record their customized classification rules for use in the future. Look out for an advanced taxonomy management feature which allows users to instantaneously reclassify data with a different schema to better accommodate the needs of any particular situation. Using advanced taxonomy management, different departments can use the same data set for distinct purposes.

When it comes to data classification, 3 main areas cannot be overlooked:

  • Context-based categorization: This type of categorization allows for the creation of custom categories based on the content of data, which means that a user doesn’t have to rely on generalized categories.
  • Category hierarchy: This method catalogs data in a highly organized structure, creating sub-categories based on the specific aspects of data. This simplifies the search and information retrieval processes.
  • Category drill-down: This is an important feature to conduct an advanced data analysis. At the touch of a button, this method allows users to drill down into categories to access associated concepts and feedback.

Thanks to advanced taxonomy management, a company can set up any combination of these or other categorization methods and use them in their current and future projects.

 7. Custom classification of data

Custom data classification allows for the creation of categories based on the data being analyzed. Personalized categories allow for better precision than derivative, cookie-cutter categories. For instance, support tickets can be cataloged depending on their specific topic, saving analysts the time and effort of having to figure out what issues they’re working on. 

8. Multi-layered filters

If you have complex data sets to analyze, make sure your text analytics provider has the ability to simultaneously combine multiple filters and view the resulting intersection of data- an analytical power previously restricted to experts using pivot tables. Complex filtering capabilities found in the most advanced text analytics platforms have the flexibility to allow users to reorder filters as they see fit, giving them the chance to explore data without requiring any further manipulation.

9. Intuitive reporting and dashboards

Reporting tools implemented on text analytics software can produce live reports supplemented by visual representations of data. Information can be accessed in real-time through an intuitive dashboard that can be filtered to rapidly visualize categorized data. Possible filter options should include date range, sentiment, area code, personal information, and more.

10. Search feature to explore data at any point in time

A smart search feature is indispensable for the efficient functioning of a text analytics platform. The most advanced search features can combine results from any number of data sets and allow for the creation of search strings to better explore complex data.

6 qualities your text analytics platform must have

  1. Ability to handle unstructured data
  2. Scale and speed
  3. Variety
  4. Pre-processing capabilities
  5. Ease of use
  6. Compatibility and integration

1. Ability to handle unstructured data

Text analytics platforms are often designed to analyze structured data (like surveys) but what happens when you need to analyze unstructured data (like social media posts)? Make sure to look for a platform that can accurately process unstructured data automatically. To test this out, you could ask your potential text analytics provider for a trial with your own data sets.

2. Scale and speed

Text data is generated at increasingly high volume and velocity — over 2.5 billion gigabytes of data every day — so an analytics platform must be able to handle large amounts of information, process it quickly, and make it available on demand. If a certain occasion calls for accentuated features, they should be accessible by simply upgrading your membership with your service provider.

3. Flexibility

Not all text data is the same or used for the same purpose. You may want to discover sentiment about a brand or product from social media posts; understand customer feedback from survey responses; identify violations of security policies from emails, or find important information buried in contracts. In each case, you need a different approach and a text analytics platform capable of understanding the parameters of your questions.

4. Pre-processing capabilities

Most text analytics platforms use NLP to analyze unstructured data and to provide analytics solutions, but NLP alone is not enough to identify relevant information and turn it into a machine-readable format. Users also need features like entity extraction, which identifies people, places, organizations, and other nouns. Not all text analytics platforms can operate in multiple languages, so knowing the pre-processing capabilities is a must.

5. Ease of use

A fast and easy-to-use interface is what you should be looking for. An intuitive interface makes it simple for analysts to learn and use the platform, navigate through the various functions and visualize results. Users want a fast upload and download of data, and the ability to handle multiple file formats.

6. Compatibility and integration

If you’re in a big data company, you’ll want to integrate text analytics to other systems in your organization, so make sure that the platform has an open architecture and APIs for easy integration with your existing applications and databases. If you’re at a startup, that means finding a solution which can run on Amazon Web Services or your favored hosting service, or integrate with the Active Directory.


Semeon’s AI-powered analytics platform checks all these boxes we have covered in this article. Before making your final decision, ask for a trial where you get to see for yourself how your data can be transformed thanks to robust text analytics. Try Semeon’s all-inclusive turnkey solution for 2 months for 2,900 CA$. We take care of everything for you: guided tour and access to the platform, data import and complete analysis by our experts, monthly custom report, one-on-one report delivery with comments.

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