• Asharani.S.Dandoti
• Prof. Amarnath Chadchankar

The performance analysis of social networks is a very impressive investigation area while a fundamental issue concerns the finding of user communities. The current work of feeling acknowledgment on Twitter explicitly relies upon the utilization of vocabularies and straightforward classifiers on bag- of-words models. The indispensable inquiry of our perception is regardless of whether we will improve their general execution utilizing 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 Sequential Minimal Optimization algorithm works on testing dataset with help of huge amount of training dataset. Measure the performance of POMS & Sequential Minimal Optimization algorithms on Twitter API. The result shows with the help of Emojis for emotion recognition using tweet contents.

Emotion Recognition, Text Mining, POMS, Recurrent Neural Networks, Convolutional Neural Networks, Unison Model, Sequential Minimal Optimization, Twitter.

"A Survey On Emotion Classification Of Tweets On Unison Model Using POMS Categories With SMO Classifier", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.763-771, June 2019, Available :http://www.jetir.org/papers/JETIR1907C69.pdf

"A Survey On Emotion Classification Of Tweets On Unison Model Using POMS Categories With SMO Classifier", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp763-771, June 2019, Available at : http://www.jetir.org/papers/JETIR1907C69.pdf