Automatic Categorization of Open-Ended Responses

Automatic categorization in the YourCX platform is a mechanism that identifies key themes in respondents’ open-text answers and assigns them to relevant topics—regardless of the language used.
This makes it easy to extract the most common subjects and check whether respondents are discussing areas such as customer service, products, pricing, or any other business-relevant domain.

This automatic analysis is directly connected to sentiment and emotion detection, so you can instantly see whether responses related to a specific topic are mostly positive, neutral, or negative, as well as which specific emotions (e.g. joy, trust, anxiety, sadness) are most often expressed.

Scalability as a Major Benefit

One of the biggest advantages of automatic categorization is its scalability.
When companies receive tens of thousands of comments monthly, manually reviewing and tagging each one becomes unmanageable.

The automatic engine processes data quickly, offering real-time insights into trends, pain points, or positive feedback.
It’s also language-independent, capable of recognizing intent and keywords across multiple languages, allowing international businesses to gather and analyze feedback from multiple markets within a single platform.

Automatic categorization also supports more efficient resource management and faster decision-making.
For instance, if the system detects that some users are criticizing new app features, while others are praising in-store service, the business can act quickly—fixing issues or enhancing strong areas.

The system also adds emotion intensity to positive or negative mentions, which helps estimate the strength of satisfaction or dissatisfaction.
Overall, the mechanism provides a clear and immediate overview of what customers are saying, even across very large datasets.

Limitations of Automatic Categorization

As with any tool, automatic categorization has its drawbacks:

• Misinterpretation by algorithms – the system may sometimes misidentify the main topic of a comment or fail to detect sarcasm, irony, or cultural nuances.
  Ambiguous responses may be assigned to the wrong topic or miss the author’s true intent.

• Multi-topic complexity – when a comment includes multiple themes, the system might focus on only one, or distribute categories evenly, potentially diluting the core message.

• Keyword dependency – models often rely on specific keywords or language structures, which can lead to missed insights from less common phrasing or industry-specific jargon.
  Ongoing improvement and monitoring can help reduce this issue over time.

• Over-categorization – in some cases, the system may generate too many narrowly defined categories, fragmenting the insights.
  This can be resolved using manual or automatic grouping of related topics.

• Need for manual adjustments – despite high accuracy, it is still important to have manual editing options to review and correct edge cases.