FAQs: Sentiment analysis

Q: What does a sentiment score of 100 represent?
A: A score of 100 is not a goal or KPI, it’s a signal of an exceptional emotional experience, and it indicates:

• The customer expressed multiple strong positive sentiments, like delight, gratitude, or praise.
• There was no negative sentiment detected during the interaction.

Q: Is it realistic to aim for a score of 100?
A: Not really. This score reflects emotionally impactful experiences, not to imply that every good interaction should score 100. Most effective, respectful interactions will score below that and still be considered excellent.

Q: So what should I aim for instead?
A: Focus on ensuring that:

• The interaction is appropriate for the context. Don’t force positive sentiment where it’s not relevant.
• In emotionally charged conversations, strive for a positive Sentiment Trend and expressions of empathy, especially toward the end.
• In purely transactional interactions, clarity, speed, and resolution may be more important than emotional tone.
• Regardless of tone, the customer should feel heard, respected, and that their issue was addressed.

Q: What’s the difference between sentiment score and sentiment trend?
A: These two metrics answer different questions:

• Sentiment score reflects how the customer felt overall, with greater weight given to their emotions at the end of the interaction. It’s a normalized weighted average of all sentiment events during the conversation.

• Sentiment trend measures how the customer’s emotional state changed over time. It calculates the difference between average sentiment at the beginning and at the end, divided by two.

Q: How should I read specific sentiment scores?
A:

• Score of 60: A lightly positive interaction. This might reflect a single positive phrase later in the call, with no major emotional highs or lows.
• Score of 80: A more sustained positive interaction. Typically, multiple positive events occurred, especially later in the call.
• Score of 100: Extremely rare. Indicates a peak emotional experience—several clear positive expressions (like praise or delight) and no negativity.

Q: What does the sentiment trend tell me?
A: It captures the emotional trajectory of the customer:

• A positive trend indicates emotional improvement.
• A negative trend may suggest increasing frustration—even if the overall score is high.

Examples:

• Score 60 + Trend +30 = Emotional improvement, mild to positive.

• Score 80 + Trend +5 = Consistently positive.

• Score 80 + Trend -20 = Started well but declined—pay attention!

For more information about fomula calculation, see Sentiment Score Formula, and Sentiment Trend Formula.

The EU AI Act restricts the use of AI systems that infer emotions in workplace and educational settings, with exceptions only for medical or safety purposes (Chapter II, Art. 5(1)(f)). This restriction is driven by concerns about the scientific validity, reliability, and potential power imbalances of such AI systems in these contexts. Recital 44 of the Act emphasizes that AI systems using biometric data to infer emotions or intentions can be intrusive and lead to discriminatory outcomes, posing risks to individual rights and freedoms.

Genesys Cloud’s sentiment analysis operates differently and does not fall under this restriction. It analyzes customer interactions based on language used, assigning sentiment scores to phrases within interaction transcripts without using biometric data. For more information on this process, see Understand sentiment analysis.

Similarly, Genesys Cloud’s Agent Empathy Analysis evaluates the language used by customer service representatives to assess empathy and emotional intelligence during interactions. This analysis focuses on the dialogue within the transcripts and provides insights into the quality of customer care without relying on any biometric data. For more information about agent empathy analysis, see Understand agent empathy analysis.

For transcripts, sentiment analysis is always automatically performed after transcription is complete, the two are interconnected. It is not possible to turn off sentiment analysis for voice transcription without also turning off voice transcription.

For digital transcripts, sentiment analysis only occurs if the expected dialect is set to one specific supported language. For more information, see the Select one or more dialects for digital interactions section in the Speech and text analytics article.

For more information, see Understand sentiment analysis.

Sentiment analysis automatically highlights the exact moment when a customer conveys positive, neutral, or negative sentiment. 

The highlighted words and/or entire phrases become actionable business insights that can be used (among numerous use cases) to:

• Track customer perception.
• Identify and reward exceptional agent performance.
• Improve customer experience.
• Improve products and services.

Overall customer sentiment score

The customer sentiment score aims to capture the level of satisfaction experienced by the customer towards your company, products, or customer service. The customer sentiment score is computed by weighing each found sentiment event (positive or negative), with greater significance placed on the sentiment events that occurred towards the end of the interaction.  In essence, the customer sentiment score answers the question, did the customer leave the interaction happy, or dissatisfied?

How is the overall customer sentiment score calculated?

The overall customer sentiment score is represented with a value that ranges from -100 to +100, with -100 being extremely dissatisfied and +100 being extremely satisfied.

The overall customers sentiment score is computed by weighing each found sentiment event (positive or negative) based on their relative location in the interaction with a greater weight towards the end of the interaction.

The relative location is calculated by taking the index of the customer’s phrase (in which the sentiment event takes place), and dividing that index by the total number of phrases on the customer side.

In the following examples, assume that each call has 42 total phrases, 20 of which are spoken by the customer.

Let’s assume the following events were detected by customer sentiment analysis:

As a result, the overall sentiment score would be f((+1 x 0.50) + (+1 x 0.90)) = f(1.40), where f is the function that normalized the customer sentiment score in the range -100 to +100. The function f is defined as Normalized Overall Sentiment Score = 100 x tanh(0.75 x Overall Sentiment Score), giving a Normalized Overall Sentiment Score of 78.

Another example:

As a result, the overall sentiment score would be f((-1 x 0.10) + (+1 x 0.40) + (-1 x 0.85)) = f(-0.55), where f is the function that normalized the customer sentiment score in the range -100 to +100. The function f is defined as Normalized Overall Sentiment Score = 100 x tanh(0.75 x Overall Sentiment Score), giving a Normalized Overall Sentiment Score of -39.

For more information, see Understand sentiment analysis.

Currently, sentiment analysis is based solely on textual content that conveys specific customer sentiment (positive, negative, neutral).

For more information, see Understand sentiment analysis.

To run sentiment analysis for digital interactions (email, chats, and messages), you must set an expected dialect (language) in the speech and text analytics settings page.

Currently, English and Spanish dialects support sentiment analysis.

For more information, see Speech and text analytics settings.

The sentiment trend is determined by comparing the sentiment events found closer to the start of the interaction, to the sentiment events found closer to the end of the interaction. For this reason, the sentiment trend may be updated when additional follow ups occur within the same interaction. There is a minimum number of customer phrases required for the sentiment trend to be calculated, usually around 6 or more customer phrases are required.

Customer sentiment trend

Sentiment events are clustered into two groups:

• Sentiment events closer to the start of the interaction are defined as start-events.
• Sentiment events closer to the end of the interaction are defined as end-events.

sentimentTrend = (sentiment score of end-events – sentiment score of start-events) / 2

The sentiment trend is presented to the user as follows:

• Improving – If the trend score is +55 it is defined as improving.
• Slightly improving – If the trend score is between +20 and +55 it is defined as slightly improving.
• No change – If the trend score is between -20 – +20 it is defined as no change.
• Slightly declining – If the trend score is between -19 and -55 it is defined as slightly declining.
• Declining – If the trend score is less than -55 it is defined as declining.

For more information, see Understand sentiment analysis.