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.

Emotion Recognition, Text Mining, Twitter, Recurrent Neural Networks, Convolutional Neural Networks, Multinomial Naïve Bayes Classifier

[1] NikoColneric and Janez Demsar, “Emotion Recognition on Twitter: Comparative Study and Training a Unison Model” IEEE Transactions on Affective Computing. PP. 1-1.10.1109/TAFFC. 2018. 2807817.
[2] J. Bollen, H. Mao, and X.-J. Zeng, “Twitter mood predicts the stock market,” J. of Computational Science, vol. 2, no. 1, pp. 1–8, 2011.
[3] J. Bollen, H. Mao, and A. Pepe, “Modelling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena”, in Proc. of the 5th Int. AAAI Conf. on Weblogs and Social Media Modelling, 2011, pp. 450-453.
[4] S. M. Mohammad, X. Zhu, S. Kiritchenko, and J. Martin, “Sentiment, emotion, purpose, and style in electoral tweets,” Information Processing and Management, vol. 51, no. 4, pp. 480–499, 2015.
[5] B. Plank and D. Hovy, “Personality Traits on Twitter or How to Get 1,500 Personality Tests in a Week,” in Proc. of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2015, pp. 92–98.
[6] X. Liu, J. Gao, X. He, L. Deng, K. Duh, and Y.-Y. Wang, “Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval,” Proc. of the 2015 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 912–921, 2015.
[7] O. Irsoy and C. Cardie, “Opinion Mining with Deep Recurrent Neural Networks,” in Proc. of the Conf. on Empirical Methods in Natural Language Processing. ACL, 2014, pp. 720–728.
[8] S. M. Mohammad and S. Kiritchenko, “Using Hashtags to Capture Fine Emotion Categories from Tweets,” Computational Intelligence, vol. 31, no. 2, pp. 301–326, 2015.
[9] B. Nejat, G. Carenini, and R. Ng, “Exploring Joint Neural Model for Sentence Level Discourse Parsing and Sentiment Analysis,” Proc. of the SIGDIAL 2017 Conf., no. August, pp. 289–298, 2017.
[10] S Kamble, SM Sangve, “Real time Detection of Drifted Twitter Spam Based On Features,” International Journal of General Science and Engineering Research (IJGSER), ISSN 2455-510X,Vol 4(1), 2018,21-23.
[11] Harshad Dattatray Markad, SMS angve, “Parallel Outlier Detection for Streamed Data Using Non-Parameterized Approach,” IJSE, Volume 8, Issue 2 July-December 2017.
[12] M. Farhoodi and A. Yari, “Applying machine learning algorithms for automatic Persian text classification,” 2010 6th International Conference on Advanced Information Management and Service (IMS), Seoul, 2010, pp. 318-323.
[13] E. Tromp and M. Pechenizkiy, “Rule-based Emotion Detection on Social Media:” Putting Tweets on Plutchik’s Wheel, arXiv preprint arXiv: 1412.4682, 2014.
[14] S. Chaffar and D. Inkpen, “Using a Heterogeneous Dataset for Emotion Analysis in Text”, in Canadian Conf. on Artificial Intelligence. Springer, 2011, pp. 6267.
[15] S. Aman and S. Szpakowicz, “Identifying Expressions of Emotion in Text, in Int. Conf. on Text, Speech and Dialogue”, vol. 4629. Springer, 2007, pp. 196205.
[16] G. Mishne, “Experiments with Mood Classification in Blog Posts”, in Proc. of ACM SIGIR 2005 Workshop on Stylistic Analysis of Text for Information Access, vol. 19, 2005, pp. 321327.