Resumen de deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets
R. Vinayakumar, B. Premjith, S. Sachin Kumar, K.P. Soman, Prabaharan Poornachandran
This working note presents the method- ology used in deepCybErNet submission to the shared task on Emotion Intensities in Tweets (EmoInt) WASSA-2017. The goal of the task is to predict a real val- ued score in the range [0-1] for a particular tweet with an emotion type. To do this, we used Bag-of-Words and embedding based on recurrent network architecture. We have developed two systems and experi- ments are conducted on the Emotion In- tensity shared Task 1 data base at WASSA- 2017. A system which uses word em- bedding based on recurrent network archi- tecture has achieved highest 5 fold cross- validation accuracy. This has used embed- ding with recurrent network to extract op- timal features at tweet level and logistic regression for prediction. These methods are highly language independent and ex- perimental results shows that the proposed methods is apt for predicting a real valued score in than range [0-1] for a given tweet with its emotion type.
