Automated Sentiment Scoring

Hi, exploring around? I’m Conviner, your call center terminology assistant, ready to help you learn more about contact centers. 

Did you know? 54% of companies use sentiment analysis today to monitor customer reviews, feedback, or social media for emotions and opinions.

1. What is automated sentiment scoring?

Automated sentiment scoring uses AI and NLP to detect emotions or opinions in customer interactions, calls, chats, emails, and assign a score based on tone. In platforms like Convin, each conversation is automatically analyzed and labeled as positive, neutral, or negative, helping businesses track customer satisfaction at scale without manual reviews.

2. How is sentiment score calculated?

Sentiment scores are calculated using machine learning models trained on large datasets. These models evaluate:

• Word choice and tone
• Voice pitch, speed, and pauses (in speech)
• Contextual patterns and emotional cues

The output is usually a numerical score (e.g., -1 to +1) or a sentiment label. Convin, for example, assigns sentiment scores to every call using voice and text-based emotion detection.

3. How accurate is sentiment analysis?

Accuracy depends on the model, context, and data quality. Modern AI systems achieve up to 85–90% accuracy in controlled environments. However, real-world accuracy can vary due to sarcasm, slang, or multilingual usage. Tools like Convin are optimized for industry-specific language (e.g., BFSI, BPO), making sentiment analysis more reliable in those domains.