Sentiment Analysis

Definition

Sentiment Analysis, also known as opinion mining, is a sub-field of Natural Language Processing (NLP) that identifies and extracts subjective information from source materials. It involves determining whether a piece of writing is positive, negative or neutral. In the context of customer support, it’s used to analyze customer feedback, reviews, and social media conversations to understand customer sentiment towards a product, service, or brand.

Importance

Sentiment Analysis is crucial in customer support as it helps businesses understand how customers feel about their products or services. This can guide improvements, address customer concerns proactively, and enhance overall customer satisfaction. It also aids in identifying trends and insights that can inform strategic decision-making.

Calculation

Sentiment Analysis is typically performed using AI and machine learning algorithms that classify text into positive, negative, or neutral categories. The sentiment score can range from -1 (very negative) to 1 (very positive).

Example

For instance, a B2B SaaS company might use Sentiment Analysis to analyze customer reviews. If a review says, ‘The software is user-friendly and has excellent features’, the sentiment analysis tool would likely classify this as positive sentiment.

Best Practices

1. Use Sentiment Analysis in conjunction with other metrics for a holistic view of customer satisfaction. 2. Regularly update your Sentiment Analysis tool to understand evolving customer language and slang. 3. Combine Sentiment Analysis with proactive support to address negative sentiments before they escalate.