Detecting and Analyzing Emotions Using Text Stream Messages

Abstract

Emotions are the gateway to converse your point of view to others. Generally, you can express your emotions either by audio or writing or symbols. Amid of audio and writing, audio/voice is a persuasive method to communicate emotions. During this work, we can get these emotions through the audio of the human using a deep learning network. We are extensively classify the emotions of the humans into irate, quiet, disdain, dread, glad, nonpartisan, miserable, and shock for effective emotion prediction. The audio features define how the audio signal reacts to the disturbing influence perceptible throughout. Thus, these features play a vital role in recognizing the emotion in audio. We are applying MFCC (Mel-Frequency Cepstral Coefficients) to the audio information to extract these essential features. MFCC is the preeminent method to extract the features from audio. Moreover, to get exact features, we are applying increase methods to the audio information.