Deep Learning for Unearthing Emotions in Twitter A Hybrid Emotional Recognition Model

With the intensification of new classes of media such as Twitter, the Internet has become a primary route for individual and interpersonal messaging. Many individuals share their thoughts regarding news-related topics on Twitter, an established SNS network built on people’s relationships. It offers us with a Source of data from which we can dig people’s thoughts, which is useful for product reviews and community monitoring. A Hybrid Emotional Recognition Model (HERM) is proposed in this research. Hashtags are recognized as the tag for emotional cataloging based on gathered posts from Twitter. Meanwhile, emoji and the N-grams are dug and used to classify the gathered topic comments into four distinct sentiment groups using the distorted emotional models. Machine learning approaches are applied of categorizing the emotional information set, yielding an 92 % accuracy result. Furthermore, entities underlying emotions might be obtained using the deep learning model SENNA.