A Study on Emotion Detection & Classification from Text using Machine Learning

Abstract

Humans are using online social networks to share their opinions and thoughts on a variety of subjects and topics with their friends, family, and relations through text, photographs, audio and video messages and posts. On specific social, national, and global topics, humans can share their thoughts, mental states, moments, and viewpoints. Given the variety of communication options available, text is one of the most popular mediums of communication on social media. The study described here aims to detect and analyses sentiment and emotion expressed by people in their messages, and then use that information to generate suggestions. Humans collected comments and replies on a few specific topics and created a dataset with text, sentiment emotion, and other data. Emotion identification from Text is a new topic of research that is closely related to sentiment analysis. Anger, disgust, fear, happiness, sadness, and surprise are examples of emotions that may be detected and understood by the expression of texts using Emotion Analysis. Emotion Detection focuses on feature extraction and word recognition because pre-processing techniques improve accuracy of classification.Humans are using online social networks to share their opinions and thoughts on a variety of subjects and topics with their friends, family, and relations through text, photographs, audio and video messages and posts. On specific social, national, and global topics, humans can share their thoughts, mental states, moments, and viewpoints. Given the variety of communication options available, text is one of the most popular mediums of communication on social media. The study described here aims to detect and analyses sentiment and emotion expressed by people in their messages, and then use that information to generate suggestions. Humans collected comments and replies on a few specific topics and created a dataset with text, sentiment emotion, and other data. Emotion identification from Text is a new topic of research that is closely related to sentiment analysis. Anger, disgust, fear, happiness, sadness, and surprise are examples of emotions that may be detected and understood by the expression of texts using Emotion Analysis. Emotion Detection focuses on feature extraction and word recognition because pre-processing techniques improve accuracy of classification.Humans are using online social networks to share their opinions and thoughts on a variety of subjects and topics with their friends, family, and relations through text, photographs, audio and video messages and posts. On specific social, national, and global topics, humans can share their thoughts, mental states, moments, and viewpoints. Given the variety of communication options available, text is one of the most popular mediums of communication on social media. The study described here aims to detect and analyses sentiment and emotion expressed by people in their messages, and then use that information to generate suggestions. Humans collected comments and replies on a few specific topics and created a dataset with text, sentiment emotion, and other data. Emotion identification from Text is a new topic of research that is closely related to sentiment analysis. Anger, disgust, fear, happiness, sadness, and surprise are examples of emotions that may be detected and understood by the expression of texts using Emotion Analysis. Emotion Detection focuses on feature extraction and word recognition because pre-processing techniques improve accuracy of classification.