Sentiment Analysis & Emotion AI, How Are They Different?
In one of our blogs, titled Effect Change by Understanding User Behavior, we have discussed the User Behaviour Flow based on using different features of software applications. Here, we are going to gain some more insight into users’ Sentiment analysis and Emotion AI.
Organizations are increasingly showing interest in Natural Language Processing (NLP) and Image Analysis solutions. It can help them understand the perceptions around their brands, solutions, or any topic they may want to better understand. Sentiment Analysis and Emotion AI are the most prominent AI techniques to address this need. Generally, many don’t have a clear understanding of the objective of these two approaches and how they differ. Perhaps this brief explanation can help.
Sentiment analysis, or opinion mining, applies Natural Language Processing (NLP) and Machine Learning (ML) techniques to identify, extract, and quantify subjective information from the text to determine the emotional tone, attitude, or opinion expressed by the writer or speaker. The objective of sentiment analysis is target-oriented meant to classify text as positive, negative, or neutral based on the presence of certain keywords, phrases, or patterns of language associated with different sentiments. Hence, this approach often finds its use to analyze product reviews, social media posts, and customer feedback. It is to gauge public opinion or sentiment about a particular brand or topic.
Emotion AI or Emotion Analysis
Emotion AI, conversely, is the process of using NLP, image analysis, audio analysis, and ML to identify, extract, and analyze emotional information from various sources, such as text, speech, images, and videos. This involves using various AI technologies and techniques to understand and interpret the feelings conveyed in analyzing the information. It goes beyond classifying into three possible outcomes (positive, negative or neutral ) sentiment. Moreover, this is to identify specific present emotions and is more involved than sentiment analysis. The analyzed outcomes represent a greater range of insight that require interpreting much more subtlety. Altogether, Emotion AI can help individuals and organizations better understand the emotional state of their customers, audiences, and stakeholders. Surveys, social media posts, and ratings may help you get a general idea about customer views but it does not provide finer, granular insights regarding what is being felt, even if it is not being said.
Watch for the next blog post in this series Thursday, where we’ll share real-world examples of Sentiment Analysis. Correspondingly, another blog on Tuesday with real-world examples of Emotion AI.