• Miss. Mayuri Nikam
• Prof. Sheetal Thokal

The overall performance analysis of social networks is a completely massive observation area while a fundamental issues about concerns the finding of user communities. The previous work of detecting emotions on Twitter depends specifically on the use of simple vocabularies and classifiers in word-bag models. The vital question of our observation is whether or not improving its general 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 gives less time consumption without interfere human labeling. The Least Square Support Vector Machine (LSSVM) classifier algorithm works on testing dataset with help of huge amount of training dataset. Measure the performance of POMS and LSSVM classifier algorithms on Twitter API. The result shows with the help of Emojis for emotion detection using tweet contents.

Emotion Recognition, Machine Learning, NLP, Recurrent Neural Networks, Convolutional Neural Networks, Unison Model, LSSVM

"EMOTION DETECTION THROUGH EMOJIS USING UNISON MODEL VERSUS LSSVM CLASSIFIER ON TWITTER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.264-268, June 2019, Available :http://www.jetir.org/papers/JETIR1906K29.pdf

"EMOTION DETECTION THROUGH EMOJIS USING UNISON MODEL VERSUS LSSVM CLASSIFIER ON TWITTER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp264-268, June 2019, Available at : http://www.jetir.org/papers/JETIR1906K29.pdf