Oversai VoCs automatically analyzes every conversation to help teams understand what customers are talking about and how they felt during the interaction.
Using AI, Oversai generates:
Conversation Topics (AI Tags) – the main subjects discussed during the interaction.
Customer Sentiment – how the customer felt throughout the conversation.
Agent Sentiment – how the support agent communicated during the interaction.
Customer Sentiment Evolution – whether the customer's emotional state improved, worsened, or remained stable from the beginning to the end of the conversation.
Conversation Summary – a concise recap of what happened during the interaction.
Together, these insights help teams monitor customer experience, identify trends, and prioritize conversations that deserve additional review.
Sentiment Analysis 😊😐😠
Each interaction is analyzed to detect emotional tone and intensity. Oversai considers three main elements:
Tone of the conversation 🎭 – whether the interaction feels friendly, neutral, or tense.
Word choice 📝 – identifying language that carries positive, negative, or neutral meaning.
Emotional intensity 🔥 – recognizing signs of frustration, enthusiasm, calmness, or indifference.
Based on these factors, Oversai assigns a sentiment score on a scale from -1 to +1:
Polarity Score | Meaning |
|---|---|
+1 | Strongly positive (happy, satisfied, grateful) |
0 | Neutral (matter-of-fact, no clear emotional tone) |
-1 | Strongly negative (frustrated, angry, upset) |
Values between these represent different levels of emotional intensity.
For example:
+0.4 🙂→ Mildly positive
-0.6 😟. → Moderately negative
+0.9 😁→ Very positive
Oversai generates two independent polarity scores for every conversation:
Customer Polarity – reflects the customer's emotional tone.
Agent Polarity – reflects how the support agent communicated throughout the interaction.
This makes it possible to identify situations where an agent remained calm and professional even when the customer was frustrated.
Alongside the score, the system also provides an easy-to-read sentiment label: Positive, Negative, or Neutral. This makes it simple for managers and analysts to track how conversations are trending over time.
How Sentiment is Calculated
Oversai uses advanced AI language models to evaluate the complete conversation rather than relying on keyword matching or predefined sentiment dictionaries.
For every conversation, the AI:
Reads the entire interaction between the customer and the agent.
Evaluates tone, wording, emotional intensity, and conversational context.
Assigns separate polarity scores for both participants on a scale from -1 to +1.
Generates an easy-to-read sentiment label (Positive, Neutral, or Negative) for both the customer and the agent.
Instead of counting positive and negative words, the AI evaluates the conversation much like an experienced QA analyst would—considering context, intent, and the overall emotional tone.
Customer Sentiment Evolution
While polarity summarizes the overall emotional tone, it doesn't always tell the full story.
A conversation may begin with an upset customer and end with a satisfied one, or the opposite may happen.
To capture this journey, Oversai analyzes Customer Sentiment Evolution.
Possible outcomes include:
Evolution | Meaning |
Improving | The customer started negatively but finished in a neutral or positive emotional state. |
Worsening | The customer started positively or neutrally but became more negative by the end of the conversation. |
Stable | The customer's emotional tone remained relatively consistent throughout the interaction. |
In addition to the evolution label, Oversai generates a brief explanation describing why the conversation was classified that way.
Why it matters
Customer Sentiment Evolution helps teams understand conversation outcomes that may not be obvious from a single sentiment score.
For example:
Improving conversations often highlight successful service recovery.
Worsening conversations may indicate unresolved issues that deserve additional review.
Stable conversations simply maintained a consistent emotional tone throughout the interaction.
Note: Sentiment Evolution is currently calculated only for the customer.
Topic Categorization 📂
In addition to sentiment, Oversai automatically detects the main topics discussed during the interaction.
Conversations are matched against a predefined list of business topics (e.g., billing, technical support, product feedback).
If a topic falls outside the existing list, Oversai can also suggest new potential categories to help expand your taxonomy.
This ensures your team has a clear understanding of what customers are talking about most often, and where process improvements might be needed.
Conversation Summary ✍️
Oversai also generates a concise summary of every conversation.
The summary highlights:
The customer's main issue or request.
The actions taken by the support agent.
The final resolution or current status.
This allows reviewers to quickly understand what happened without reading the full conversation.
How to Interpret the Results 🚀
The best insights come from combining all AI outputs rather than reviewing each one independently.
Some best practices include:
Compare Customer Polarity and Agent Polarity to understand whether the agent maintained a positive and empathetic tone during difficult conversations.
Review Customer Sentiment Evolution to identify successful recoveries or interactions that deteriorated over time.
Use AI Tags to understand why customers contacted support.
Read the Conversation Summary for additional context before making quality or operational decisions.
For example:
Customer Polarity: -0.82
Agent Polarity: +0.85
Customer Sentiment Evolution: Improving
This indicates the customer initially felt frustrated, while the agent maintained a positive tone and successfully improved the customer's experience.
Good to Know
Sentiment analysis is AI-generated and may vary slightly between analyses.
Polarity scores are intended to represent emotional tone, not exact measurements.
AI Tags, Sentiment, Sentiment Evolution, and Conversation Summaries should be interpreted together to obtain the most complete understanding of a conversation.
As with any AI-generated insight, occasional edge cases may occur, especially in conversations containing sarcasm, ambiguity, or limited context.
Why It Matters
With Oversai’s VoC analysis, your organization can:
Quickly spot frustrated or dissatisfied customers before issues escalate ⚠️.
Identify agent performance trends and areas for coaching 📊.
Monitor common topics and pain points raised by customers 🗣️.
Go beyond traditional survey-based insights (like CSAT or NPS) 🙌 — since many customers don’t respond to surveys, Oversai ensures you still capture their true feedback directly from conversations.