Sentiment analysis is a sub-category of natural language processing (NLP) and machine learning (ML) that extracts emotions, thoughts, and opinions from audio or textual data.

In short, sentiment analysis enables bots to comprehend the user’s mood and sentiments by analyzing utterances for words and phrases that indicate a particular emotion. The bot can then model its response suitably to provide excellent customer service. Using machine learning tools to train the bot with words/phrases that correlate to different moods, sentiment analysis teaches the bot to detect emotions without any human intervention.

For example, A customer writes, “I am annoyed with your team. I have been sent items that I didn’t order.”

The bot responds by saying, “We are incredibly sorry for the mistake. The correct package will be delivered today by 16:30 hours.

Here, sentiment analysis renders emotional intelligence to the bot and allows it to understand that the phrase annoyed is an expression of the customer’s disgust and anger at the callousness of the team.

Hence, sentiment analysis helps bots conduct thoughtful, engaging, and meaningful conversations with customers.

Here’s how the process flows –

• The bot starts with understanding the polarity of the conversation. Polarity indicates whether the sentiment displayed in the conversation is positive, negative, or neutral. The bot can detect emotions, such as joy, anger, sadness, fear, disgust, curiosity, and many more.
• Then the AI and NLP technologies measure the magnitude or intensity of the emotion and assign a numerical score to each of the sentiments.
• The final score helps the bot to decide a further course of action. For example, for an utterance (voice/textual) that has a high positive score (joy/happiness), the bot can leverage the opportunity to upsell. On the other hand, for a conversation with a high negative score (sadness/anger), the bot may escalate the call to a live agent for effective remediation of the issue.