{"id":31062,"date":"2026-09-11T14:20:33","date_gmt":"2026-09-11T12:20:33","guid":{"rendered":"https:\/\/zestmeup.com\/?page_id=31062"},"modified":"2026-09-11T14:20:35","modified_gmt":"2026-09-11T12:20:35","slug":"semantic-analysis-understand-what-your-employees-are-really-saying","status":"publish","type":"page","link":"https:\/\/zestmeup.com\/en\/solution\/semantic-analysis-understand-what-your-employees-are-really-saying\/","title":{"rendered":"Semantic Analysis: Understand What Your Employees Are Really Saying"},"content":{"rendered":"\n
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Semantic Analysis<\/mark>: Understand What Your Employees Are Really Saying<\/h1>\n\n\n\n

Verbatim comments that reveal what scores don’t show.<\/p>\n\n\n\n

A survey of 1,000 employees generates hundreds of open-ended responses. No one reads them all. The most valuable signals, the ones no closed question had anticipated, are precisely the ones that get lost.<\/p>\n\n\n\n

Zest’s semantic analysis reads every single open-ended response and delivers what matters most: the themes raised, their weight, their tone, and what deserves immediate attention.<\/p>\n\n\nRequest a demo<\/span> \r\n\t

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Trusted by more than 400 HR Directors<\/div>
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Closed questions<\/mark> measure. Open-ended responses<\/mark> explain.<\/h2>\n\n\n\n
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Closed questions frame the measurement. Open-ended responses reveal what no scale could ever capture: a frustration taking root, tension within a team, a resignation signal that nothing else would have surfaced.<\/p>\n<\/div>\n\n\n\n

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Zest’s semantic analysis reads every single open-ended response and delivers what matters most: the themes, their weight, their tone, and what deserves immediate attention.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Counting words <\/mark>isn’t enough<\/h2>\n\n\n\n
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A word cloud counts occurrences: it will tell you that “workload” comes up 47 times. Semantic analysis understands meaning: it distinguishes “the workload is well balanced” from “the workload is becoming unmanageable,” links each comment to a theme, measures its tone, and cross references all of it with recurrence.<\/p>\n<\/div>\n\n\n\n

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The result isn’t a list of words, it’s a reading: what topics your employees are raising, what they’re saying about them, and in what order to address them.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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What Zest does with your verbatim comments<\/mark>
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\"Ic\u00f4ne\"Categorize<\/span><\/h3>

Every comment is automatically linked to the themes it touches on, along with its associated sentiment.<\/p>

Thematic categorization<\/h3>

Sentiment analysis<\/h3><\/div><\/div>

\"Ic\u00f4nePrioritize<\/span><\/h3>

Themes are plotted on a matrix crossing recurrence and tone. HR and managers know immediately where to focus their attention.<\/p>

To prioritize<\/h3>

To value<\/h3>

To monitor<\/h3>

To develop<\/h3><\/div><\/div>

\"Ic\u00f4neAlert<\/span><\/h3>

Sensitive comments are detected and flagged as alerts without waiting for the campaign to end. The signal never goes unanswered, and anonymity is never compromised.<\/p>

Real time alert<\/h3>

Psychosocial risks<\/h3>

Anonymity guarantee<\/h3><\/div><\/div>

\"Ic\u00f4neTranslate<\/span><\/h3>

Every employee expresses themselves in their own language, every reader analyzes in theirs. Verbatim comments are translated automatically, without delay.<\/p>

36 languages<\/h3><\/div><\/div><\/div><\/div>\n\n\n
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AI<\/mark> trained on the language of work<\/h2>\n\n\n\n

A team of NLP PhDs and occupational psychologists trained Zest’s models specifically on the language of work: indirect phrasing, sensitive topics expressed in half words, words whose meaning shifts with context. This is what makes it possible to distinguish an awkward turn of phrase from a genuine sign of distress, or a passing frustration from a structural irritant.<\/p>\n\n\n\n

The AI operates transparently and under your control. Every theme remains verifiable: one click gives access to the source comments. The frameworks are configured question by question. The AI proposes a structure, HR and managers keep the judgment.<\/p>\n<\/div>\n\n\n\n

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Three situations where semantics change the picture<\/h2>\n<\/div>\n<\/div>\n\n\n\n
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A score drops with no explanation<\/h3>\n\n\n\n

An entity’s engagement score loses a point between two campaigns. Closed questions record the drop, verbatim comments reveal the cause: the matrix surfaces a new theme, absent from the previous campaign, concentrating the negative comments.<\/p>\n<\/div>\n\n\n\n

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An alarming comment in the crowd<\/h3>\n\n\n\n

Among 800 responses, one employee mentions burnout. Without automatic detection, this comment would be read weeks later, or never. With the alert, HR opens an anonymous conversation that same day.<\/p>\n<\/div>\n\n\n\n

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A campaign across 6 countries<\/h3>\n\n\n\n

Verbatim comments come in across 5 languages. Automatic translation and unified categorization enable consolidated analysis, with no translation provider and no delay.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Your employees are writing to you. Really read them<\/mark>.<\/h2>\n\n\n\n

Real time analysis. Fast decoding. Proprietary AI.<\/em><\/p>\n\n\nRequest demo<\/span> \r\n\t

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Q&A<\/h2>\n\n\n\n

Frequently asked questions about Zest’s survey and social barometer software<\/p>\n<\/div>\n\n\n\n

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Does semantic analysis compromise respondent anonymity?<\/strong>

No. The analysis focuses on comment content, never on respondent identity. Campaign anonymity rules apply in full, and the open conversation that follows a sensitive comment preserves the employee’s anonymity unless they choose to lift it themselves.<\/p> <\/div>

What happens when a sensitive comment is detected?<\/strong>

An alert is sent to authorized personnel, without waiting for the campaign to end. They can open an anonymous conversation with the employee or send them a message pointing to internal support resources.<\/p> <\/div>

Does the AI replace human reading?<\/strong>

No, it makes it possible. Across hundreds of verbatim comments, exhaustive human reading simply isn’t feasible in practice: the AI categorizes and prioritizes so that human attention goes where it has the most value. The decision always remains with HR and the manager.<\/p> <\/div>

Does Zest’s AI use our data for training?<\/strong>

No, the continuous improvement of our models does not rely on client data. Zest’s AI is proprietary, developed in house by a team led by PhDs in NLP and psychometrics. Your data is hosted in France and never leaves the Zest ecosystem: it is never shared with any external model.<\/p> <\/div>

Can Zest be used without AI features?<\/strong>

Yes. AI features can be fully disabled for organizations that prefer it, without losing access to the rest of the platform: analytics, comparisons, exports, and dashboards all remain available.<\/p> <\/div>

What does the AI actually do in the analysis of results?<\/strong>

It steps in where manual analysis costs the most time: categorizing verbatim comments, detecting sensitive comments, summarizing interviews, and suggesting action plans. It prioritizes and prepares, but never decides: the manager and HR retain full control.<\/p> <\/div> <\/div>\n<\/div>\n<\/div>\n\n\n\n

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See Zest in action<\/span><\/h2><\/div>\n\n\n

The platform to turn feedback into impact<\/p>\n\n\nRequest a demo<\/span> \r\n\t

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<\/p>\n","protected":false},"excerpt":{"rendered":"

Semantic Analysis: Understand What Your Employees Are Really Saying Verbatim comments that reveal what scores don’t show. A survey of 1,000 employees generates hundreds of open-ended responses. No one reads them all. The most valuable signals, the ones no closed question had anticipated, are precisely the ones that get lost. Zest’s semantic analysis reads every […]<\/p>\n","protected":false},"author":20,"featured_media":30092,"parent":15909,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"inline_featured_image":false,"footnotes":""},"class_list":["post-31062","page","type-page","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/zestmeup.com\/en\/wp-json\/wp\/v2\/pages\/31062","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/zestmeup.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/zestmeup.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/zestmeup.com\/en\/wp-json\/wp\/v2\/users\/20"}],"replies":[{"embeddable":true,"href":"https:\/\/zestmeup.com\/en\/wp-json\/wp\/v2\/comments?post=31062"}],"version-history":[{"count":6,"href":"https:\/\/zestmeup.com\/en\/wp-json\/wp\/v2\/pages\/31062\/revisions"}],"predecessor-version":[{"id":31550,"href":"https:\/\/zestmeup.com\/en\/wp-json\/wp\/v2\/pages\/31062\/revisions\/31550"}],"up":[{"embeddable":true,"href":"https:\/\/zestmeup.com\/en\/wp-json\/wp\/v2\/pages\/15909"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/zestmeup.com\/en\/wp-json\/wp\/v2\/media\/30092"}],"wp:attachment":[{"href":"https:\/\/zestmeup.com\/en\/wp-json\/wp\/v2\/media?parent=31062"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}