Businesses use different types of sentiment analysis to understand how their customers feel when interacting with products or services.
Fine-grained scoring
Fine-grained sentiment analysis refers to categorizing the text intent into multiple levels of emotion. Typically, the method involves rating user sentiment on a scale of 0 to 100, with each equal segment representing very positive, positive, neutral, negative, and very negative. Ecommerce stores use a 5-star rating system as a fine-grained scoring method to gauge purchase experience.
Aspect-based
Aspect-based analysis focuses on particular aspects of a product or service. For example, laptop manufacturers survey customers on their experience with sound, graphics, keyboard, and touchpad. They use sentiment analysis tools to connect customer intent with hardware-related keywords.
Intent-based
Intent-based analysis helps understand customer sentiment when conducting market research. Marketers use opinion mining to understand the position of a specific group of customers in the purchase cycle. They run targeted campaigns on customers interested in buying after picking up words like discounts, deals, and reviews in monitored conversations.
Emotional detection
Emotional detection involves analyzing the psychological state of a person when they are writing the text. Emotional detection is a more complex discipline of sentiment analysis, as it goes deeper than merely sorting into categories. In this approach, sentiment analysis models attempt to interpret various emotions, such as joy, anger, sadness, and regret, through the person's choice of words.