Emotion extraction from text: Analysis of four different algorithms
Nimisha Jadav, Komal Shirsat, V Preethi, Mohan Bonde · Journal of Emerging Technologies and Innovative Research · 2020
Emotions play an important role in human life. In today’s internet world textual data has been proven as a main medium of communication in human-human and human-machine interaction. This increase in use of text for communication makes it a need to find the emotion present in textual data. This makes it important to provide a framework that can recognize the emotions present in the communication or the emotions of the users. Emotion recognition or sentiment analysis is a natural language processing task that mine information from textual data like twitter tweets, blogs, movie reviews and reviews from online websites and classify on the basis of polarity. Emotion Recognition Model extracts emotion from text at the sentence level. Our method uses 4 different deep learning algorithm and detects emotion from a text-input. To make the recognition more accurate, emotion-affect-bearing words and phrases were also analyzed. This experiment shows that the method could generate a good result for emotion detection from text input.