Interpreting Employee Survey Comments

Comments are where employees explain their experience in their own words. They add the context, language, and ideas that scores alone can’t provide. They’re also easy to misread. A single vivid story, a handful of strong voices, or our own expectations can quietly shape what we take away.

This guide draws on industrial-organizational psychology, survey research, and behavioral science to help you read comments with discipline, whether you’re a manager reviewing a few dozen comments from your team or an analyst working through thousands with AI. The four principles apply to everyone.

Four principles for comment analysis

1. Scores first

Your driver analysis should be the primary guide for where to focus, supported by trends, comparisons, and other quantitative signals. Comments then help you understand what employees are experiencing within those priority areas and what actions may address it. A frequent, emotional, or highly visible comment theme should not displace a focus area identified through the quantitative data, with the exception of serious issues (see principle 3).

2. Comments reflect those who chose to write

Comments are usually optional, so the employees who choose to write them may not represent everyone who completed the survey (Rogelberg & Stanton, 2007). Research also suggests that less satisfied employees may be more likely to leave comments, which can make the overall tone of comments more negative than the quantitative ratings (Poncheri et al., 2008). That does not make the comments less useful, but it changes how they should be interpreted. Use them to understand the experiences, examples, and perspectives employees chose to share, not to estimate how common an experience is across the team or organization.

3. Look for patterns before stories

Some comments naturally stand out more than others. Specific or emotional examples are easier to remember, which can make them feel more common or more important than they actually are (Tversky & Kahneman, 1973). Negative experiences also tend to draw more attention than positive ones (Baumeister et al., 2001). To keep that from shaping your interpretation, start with the patterns that recur across comments, then use individual verbatims to add context and illustrate what those patterns look like.

Frequency is still only one signal. A theme mentioned often is not automatically the highest priority, and a less common issue, such as safety or misconduct, may warrant attention because of its seriousness rather than how often it appears.

4. Separate what was said from what it means

Separate what employees said, what you think it means, and what you think should happen next. Those are three different steps, and each one adds more interpretation. People naturally fill in missing context with their own explanations and can over-attribute experiences to individual people rather than the situation around them (Ross, 1977). Keeping observation, interpretation, and action distinct makes it easier to see where the evidence ends and judgment begins, and to test whether your conclusion is well supported. Making those steps visible keeps your conclusions honest and open to challenge.

Key elements of comment analysis

Before you read the comments themselves, Perceptyx standardizes the text for analysis, including translating comments into English when needed, and classifies each comment across several lenses. Each lens is designed to answer a different question about the feedback.

Your goal: disciplined sensemaking

With a team’s comments, you can usually read every one. The challenge isn’t too much data; it’s too little. In small groups, one or two voices can look like a trend, and we tend to read patterns into small numbers that wouldn’t hold up in a larger sample (Tversky & Kahneman, 1971). Your goal isn’t formal research. It’s to understand your team’s experience without letting one comment, one memorable story, or your own expectations define the results.

1. Start with the drivers

Begin with the quantitative results, especially the drivers and priority focus areas most connected to the outcome you want to improve. Then use trends, comparisons, and lower-scoring items to add context. Choose one or two focus areas before opening the comments so the qualitative feedback helps you understand those priorities rather than creating a separate agenda.

2. Use themes and intents to find patterns

Look for themes that recur across comments, especially those connected to your focus areas. Then use intents to understand what employees are trying to communicate, such as suggestions for improvement, concerns, or strengths worth protecting.

3. Use sentiment to guide where you look

Sentiment can help you see whether a topic is discussed mostly positively, negatively, or neutrally. Use it to decide where a closer read may be useful, not as a measure of overall team morale.

4. Read the verbatims for context

Within each focus area, read across the comments rather than relying on the most striking examples. Look for the specific behaviors, processes, and conditions beneath a broad theme. “Communication is an issue” might mean information arrives late, priorities change without explanation, or leaders send inconsistent messages. That specificity is what helps turn a focus area into action.

5. Take it back to your team

Comments can clarify the issue, but your team can add the context needed to act. Share the broader themes rather than individual quotes, then ask what the pattern looks like in day-to-day work and what would help. This protects confidentiality, fills in gaps, and shows employees their feedback is being used.

Use pattern, context, action

Common traps and what to do instead

Before you act, ask four questions

Your role: direct and verify the analysis

With thousands of comments, AI makes the data much easier to navigate. It can organize feedback, surface recurring patterns, compare groups, and quickly connect you to relevant verbatims. That shifts the analyst’s role from reading or coding every comment to guiding the analysis, testing what emerges, and verifying the evidence behind it. Research on AI-assisted qualitative analysis supports this approach: AI can make the work faster and more scalable, but human judgment remains essential for interpreting nuance, contradictions, context, and sensitive findings (Foster, 2026; Tai et al., 2024; Misra et al., 2026).

One risk to actively manage is automation bias, our tendency to put too much trust in automated output, especially when it is presented clearly and confidently (Parasuraman & Manzey, 2010). A polished AI summary can still miss context, overstate a pattern, or overlook conflicting evidence. Treat AI-generated findings as signals to examine, not conclusions to accept at face value.

Know your tool

Your organization may have either Comment Copilot or Narrative Analysis Agent enabled. Both are available to event administrators and creators.

A six-step workflow for large-scale analysis

1. Frame a question from your quantitative results

Begin with a result that needs more context, such as a driver, priority area, lower-scoring item, meaningful change, or group difference. Then frame a neutral question for the comments, such as: “What experiences do employees describe related to confidence in senior leadership?”

2. Define the population

Decide what comments should be included before you begin the analysis. Document the comment question, employee group, and any filters such as theme, sentiment, or intent. A pattern within one filtered population should not be generalized to the broader workforce.

3. Generate a first pass

Use Comment Copilot or Narrative Analysis Agent to surface the initial themes and patterns. Treat this as a starting point for analysis, not the final answer.

4. Interrogate the pattern

Look at what supports the pattern, what complicates it, and whether employees are describing one experience or several different ones. Explore differences across relevant groups and push broad themes such as “leadership” or “workload” toward the specific behaviors, processes, and conditions employees describe.

5. Connect it back to the scores

Ask how the comments help explain the original result. Do they clarify what employees are experiencing, reveal meaningful differences between groups, or point toward practical actions? When the qualitative and quantitative evidence reinforce one another, confidence in the interpretation grows. When they do not, treat that as a signal to explore further.

6. Verify before you share

Before elevating a finding, review the underlying comments, including comments that do not fit the dominant pattern. Confirm the population behind the analysis and make sure the language of the finding stays within what the evidence supports. Give sensitive, surprising, or high-stakes findings an additional human review.

Ask better questions of Narrative Analysis Agent

How you phrase a question shapes the answer you get, just as it does in a survey. AI models tend to accept the premise of a question, so neutral, specific questions produce more trustworthy answers.

Biases to manage at scale

Use calibrated language

The words you use when sharing findings signal how strong the evidence is. Match your language to what the comments can actually support.

The standard for good comment analysis

Whether you’re working with 20 comments or 20,000, good analysis can answer four questions:

1. What are employees describing?
2. How consistent is the pattern?
3. Whose comments, and which data, does the conclusion represent?
4. What other evidence supports or challenges it?

For managers, that means resisting the urge to react to individual comments and using the Comments Report to understand your team’s experience in context. For admins and analysts, it means using AI to make large datasets navigable while keeping the population, the evidence, and human judgment visible throughout.

Key references

Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. Review of General Psychology, 5(4), 323–370.

Foster, S. (2026, September 22). PYX-Voice Leaderboard Refresh: What 13 New Models Tell Us About AI and Employee Surveys. PYX Labs.

Misra, R., Dahal, R., Kirk, B., Khan, R., Dogan, G., Chataut, R., & Gyawali, P. (2026). Large language models in qualitative analysis: Comparing traditional and researcher-interpreted approaches. International Journal of Qualitative Methods.

Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175–220.

Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52(3), 381–410.

Poncheri, R. M., Lindberg, J. T., Thompson, L. F., & Surface, E. A. (2008). A comment on employee surveys: Negativity bias in open-ended responses. Organizational Research Methods, 11(3), 614–630.

Rogelberg, S. G., & Stanton, J. M. (2007). Understanding and dealing with organizational survey nonresponse. Organizational Research Methods, 10(2), 195–209.

Ross, L. (1977). The intuitive psychologist and his shortcomings: Distortions in the attribution process. Advances in Experimental Social Psychology, 10, 173–220.

Tai, R. H., Bentley, L. R., Xia, X., Sitt, J. M., Fankhauser, S. C., Chicas-Mosier, A. M., & Monteith, B. G. (2024). An examination of the use of large language models to aid analysis of textual data. International Journal of Qualitative Methods, 23.

Tversky, A., & Kahneman, D. (1971). Belief in the law of small numbers. Psychological Bulletin, 76(2), 105–110.

Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207–232.

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.