Emotion Identification between POMS and Multinomial Naive Bayes Algorithm Using Twitter API - Dandoti, Asharani S; Sangve, Sunil M (International Journal of Computer Sciences and Engineering, 2019)
The analysis of social networks is a very challenging research area while a fundamental aspect concerns the detection of user communities. The existing work of emotion recognition on Twitter specifically depends on the use of lexicons and simple classifiers on bag-of words models. The vital question of our observation is whether or not we will enhance their overall performance using machine learning algorithms. The novel algorithm a Profile of Mood States (POMS) represents twelve-dimensional mood state representation using 65 adjectives with combination of Ekman’s and Plutchik’s emotions categories like, anger, depression, fatigue, vigour, tension, confusion, joy, disgust, fear, trust, surprise and anticipation. These emotions classify with the help of text based bag-of-words and LSI algorithms. The contribution work is to apply machine learning algorithm for emotion classification, it takes less time for classification without interfere human labeling. The Multinomial Naïve Bayes classifier works on testing dataset with help of huge amount of training dataset. Measure the performance of POMS & Multinomial Naïve Bayes algorithms on Twitter API. The result shows with the help of Emojis for emotion recognition using tweet contents.