Emotion and Sentiment Analysis using Machine Learning

Abstract

Emotions are strong feelings, that human develop, when they are exposed to various situations, relations, and practices. These emotions are articulated with facial expressions and gestures. Machine learning solutions are widely being used to capture these emotions. This paper in particular focuses on Emotion and Sentiment detection based on Machine Learning Techniques. Facial Detection and Semantic Analysis are the key steps involved in detection of emotions. Sentiments are classified into positive (happy, excited and funny), negative (anger, disgust and sad) and neutral. The algorithms used identify the expression by plotting points on the detected human faces and compares the same with the facial expressions in the database repository. Semantic analysis, on the other hand uses algorithms to detect emotions in the language of script and audio formats, by matching keywords, affiliating them to positive or negative connotations and aggregating individual affiliations to present an overall tone.