At Verbal, we use cutting-edge sentiment analysis technology to help healthcare organizations assess the emotional tone behind spoken or written communications. Understanding this emotional tone is crucial for evaluating call quality and improving interactions with patients and staff.

What is Sentiment Analysis?

Sentiment analysis is a natural language processing (NLP) technique that determines whether a piece of text or speech expresses positive, negative, or neutral emotions. By analyzing sentiment, Verbal helps healthcare teams measure communication effectiveness, providing actionable insights to enhance both patient and staff satisfaction.

How Does Verbal’s Sentiment Analysis Work?

Verbal’s sentiment analysis focuses solely on the words used during the conversation. It assigns a sentiment score based on the language and phrases spoken or written, extrapolating the overall tone from the words alone. It's important to note that this analysis does not factor in voice tone, affect, or other audio cues like volume or pitch.

How are Sentiment Scores Determined?

Our model uses a score-based system to evaluate the sentiment in a conversation. Sentiment scores range from -1 to 1, where:

-1 represents the most negative sentiment

0 represents neutral sentiment

1 represents the most positive sentiment

Understanding the Sentiment Scale

Extremely Negative - 😡
Very Negative - 😭
Moderately Negative - 😰
Slightly Negative - 🙁
Neutral - 😕
Slightly Positive - 🙂
Moderately Positive - 😀
Very Positive - 😍
Extremely Positive - 🥰

Why Does Sentiment Matter?

Monitoring sentiment helps identify moments of frustration, satisfaction, or confusion in conversations. Verbal’s AI analyzes these sentiment shifts and provides real-time insights, empowering healthcare organizations to:

• Improve patient care
• Enhance staff communication
• Address concerns more efficiently
• Increase overall satisfaction and retention

How Accurate is the Sentiment Analysis?

Our sentiment analysis model has been trained on large datasets, ensuring high accuracy in most cases. However, like all models, it may not always perfectly capture nuanced emotions or sarcasm. We continuously refine the model to ensure its effectiveness in the healthcare environment.

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