From Words to Emoticons: Deep Emotion Recognition in Text and Its Wider Implications
Rafal Rzepka; Mitsuru Takizawa; Jordi VallverdÄ²u; Michal Ptaszynski; Pawel Dybala; Kenji Araki
International Journal of Computational Linguistics Research, Vol. 9, No. 1, 2018.
DOI: 
Link: http://www.dline.info/jcl/fulltext/v9n1/jclv9n1_2.pdf
Abstract:
This paper summarizes several lexical methods for more comprehensive affect recognition in text using
an example of typed utterances. We introduce a set of algorithms that are capable of recognizing emotions of userâ€™s
statements in order to achieve more effective and smoother human-machine conversation. Aspects often neglected
by existing systems working with Japanese language, e.g. compound sentences, double negation sentences, modifiers
as adverbs and emoticons were combined and their higher effectiveness in recognizing affect in more complicated
sentences was confirmed through evaluation experiments. The results are introduced together with separate analysis
of emoticonsâ€™ influence on emotional load. We also discuss importance of predicting human emotions not only in the
field of human-computer interaction but also its meaning for developing ethical chatbots.