How to analyze employee survey open text data

Summary:

Analyzing open-ended survey comments means turning unstructured sentences into themes, sentiment, and a prioritized action list a manager can use. It is different from scoring closed questions, since there is no scale to average, just language to interpret.

Two methods exist to do this:

Most teams end up using a mix of both, depending on volume and available time.

This guide covers the open-text methodology specifically. For the broader question of how AI is showing up across HR work overall, our overview of HR trends and opportunities covers that wider landscape.

Manual coding still makes sense for small datasets, sensitive topics that need a careful human read, or when you need full certainty over how a theme was defined. It is slow, but it gives you complete control over category definitions.

AI-assisted analysis is the better choice once comment volume grows past what one person can carefully read, or when you have no dedicated analyst time to spend on coding. It trades some manual control for speed and consistency across large datasets.

Open-text comments are one input into a bigger picture that also includes your scored questions. Before diving into comment coding, it helps to have already reviewed your closed-question results, since a theme that shows up in comments often explains a score you already saw.

Our guide on analyzing and interpreting employee engagement survey results covers that closed-question groundwork, while this page focuses specifically on the open-text half of the analysis.

Manual coding follows a consistent sequence regardless of survey size. The goal is to move from raw text to a small set of well-defined, non-overlapping themes.

This process is still the right choice when precision matters more than speed, such as a small executive-team survey where every comment carries weight. It becomes impractical fast once comment counts climb into the thousands.

Two habits keep manual coding honest.

You do not have to pre-define categories. Emergent, bottom-up theme discovery lets themes surface from the language people actually use, rather than forcing comments into categories chosen before you read a single response.

Bottom-up discovery matters most for open-ended, unprompted questions like "what else should we know," where a pre-set category list would miss topics you did not anticipate.

AI-assisted text analysis tools are built for exactly this case, since they can cluster similar language into themes without a human building the category list first.

AI text analysis tools group similar comments into themes using natural language processing, then generate a plain-language summary of what each theme represents. Sentiment analysis runs alongside this to classify each comment as positive, negative, or neutral.

SurveyMonkey's AI survey analysis feature set includes Thematic Analysis and Sentiment Analysis, which together let you see both what people are talking about and how they feel about it, without reading every comment individually.

Yes. AI-assisted analysis is designed to surface the most common themes on its own, ranked by how often they appear, without a person defining categories in advance.

That is the core difference from manual coding, where the category list has to exist before tagging starts.

A human should still review the AI-generated themes for accuracy before sharing results widely, since automated grouping can occasionally merge related-but-distinct issues into one theme.

If a theme feels like it is mixing two separate issues, split it before presenting results to managers, since a muddled theme leads to a muddled action plan.

Action planning follows a simple chain: theme, then action, then owner.

AI can accelerate the first step by ranking themes by frequency and sentiment, so you know which issues affect the most people and feel the most negative.

From there, the work becomes human again. Pair each top theme with one specific action and name an owner who is accountable for it, rather than leaving broad themes like "communication" without a next step.

Yes, sentiment-based alerts can flag concerning language in open-text comments even when a closed-question score still looks acceptable.

This matters because a team can score fine on a numeric engagement scale while a handful of comments describe a serious, specific problem.

Setting up a review process for flagged comments, rather than relying only on dashboard scores, catches issues earlier than waiting for scores to drop on their own.

Without analyst time, lean on frequency and sentiment to do the prioritization for you.

Sort AI-generated themes by how often they appear and how negative the sentiment score is, then focus action planning on the handful that rank highest on both.

Resist the urge to act on every theme at once. Three prioritized actions that actually get done build more trust than ten themes that get acknowledged and then forgotten.

If two themes tie on both frequency and sentiment, use feasibility as the tiebreaker. A theme with a clear, achievable fix in the next quarter is often a better first move than a theme that requires a multi-year structural change, even if the second theme feels more important on paper.

Going from raw comments to a manager-ready action plan does not require choosing analyst time you do not have. Combine emergent theme discovery, sentiment tagging, and a simple theme-to-action-to-owner chain to move fast without losing nuance.

See how AI reads your open-text so you don't have to and turn employee comments into themes, sentiment, and next steps with SurveyMonkey.