The metric leads to the conversations behind the change
A percentage alone does not explain the problem. The module links the emotional signal to the topic and lets you open representative excerpts from the interaction.
Growing tension around parking
Messages sent before arrival show increasing frustration about the night-time entry instructions and waiting for confirmation.
Cause ready for review
AI shows the trend, conversation excerpts and the point at which the tone became negative — most often after an incomplete first response.
- sentiment by topic and channel
- change in tone throughout the conversation
- links to source material
An operational signal, not a diagnosis of human emotion
The analysis helps identify topics prompting positive or negative reactions. The result is a language-based approximation and must be read in the context of the conversation.
Classifies sentiment
It recognises the overall tone and changes during the interaction, while preserving the level of uncertainty.
Links emotion to the topic
It separates results across areas such as booking, breakfast, parking and guest service.
Leads to the source
It lets you move from the chart to the conversations and short survey responses behind the result.
How is sentiment analysis created?
Collect interactions
The module retrieves conversations from connected channels and optional short survey responses.
Identify topics
AI assigns excerpts to services, stages of the stay and operational matters.
Assess the tone
It determines sentiment and changes in sentiment, retaining information about ambiguous cases.
Trend with sources
It aggregates results by time and channel, linking each metric to specific conversations.
Conversation context matters more than a single word
The model analyses complete excerpts and their topic, rather than a simple list of positive or negative expressions. Results can be filtered and manually verified.
What does AI use?
- conversations from supported channels
- topics and stages of the interaction
- optional micro-survey responses
- analysis period and filters
What does the team receive?
- sentiment by topic and channel
- trend and change in tone during the conversation
- sample source conversations
The result indicates a direction; it does not pass judgement
The team can check sources, correct incorrect classifications and interpret the trend alongside its operational context. A single assessment should not trigger an automatic decision about a guest.
Staff set topics and alert thresholds, verify representative conversations and decide how to respond to the detected issue.
The module does not diagnose mental states, determine a person’s intentions with certainty, profile guests for business decisions or respond automatically based on sentiment alone.
Problems become visible before they turn into a stream of complaints
Growing tension around a service or stage of the interaction appears in the hub alongside its sources.
The team knows what the sentiment relates to, instead of viewing a single average for the entire hotel.
Every metric can be verified against the conversations behind the analysis.
Conversations and surveys feed one shared analysis
The sources are the communication channels shown in the dashboard, optionally supplemented by short survey questions linked to a specific point in the stay.
Related process elements
Frequently asked questions
Does the analysis work in multiple languages?
It can work in the languages supported by the implementation, but the quality of each version should be verified using real conversations.
Can the result be checked?
Yes. Metrics link to specific conversations and excerpts that influenced the result.
Does AI always recognise tone correctly?
No. Irony, abbreviations and cultural context can be ambiguous, so the system shows the sources and allows corrections.
Does a negative signal automatically trigger a response?
No. It can generate an alert, but the team chooses how to respond, or follows a separately approved process.