Understanding Sentiment Analysis With Social Listening and Monitoring

Sentiment analysis is an important metric that analyzes the online brand mentions and gives you insights into how your potential customers perceive your brand.

Sentiment analysis, also known as opinion mining, is the process that uses natural language processing (NLP) and machine learning (ML) techniques to analyze the online written pieces and determine the emotional intent behind them.

Since customers give their feedback more openly than ever before, sentiment analysis is becoming a powerful tool for social media monitoring and understanding their opinions and social media conversations. This way, brands learn what makes customers happy or angry so that they can tailor their products and services according to customers’ needs.

There are four types of sentiment analysis:

• Fine-grained: Analyzing reviews and ratings and classifying them into positive, negative, and neutral.

• Aspect-based: Identifying the opinions on a specific aspect of a product or service. For example, TripAdvisor uses this approach to identify the sentiment behind the customer feedback and the service itself.

• Intent-based: Identifies the intention of the customer—whether they want to purchase something or just browse.
• Emotion-based: Identifies the emotion of a customer behind the mention. This can include sadness, anger, happiness, satisfaction, frustration, etc.

Radarr identifies and analyses the precise sentiment behind behind a comment or conversation. It leads to better understanding and consequently better decisions.