FromWords to Emoticons: Deep Emotion Recognition in Text and Its Wider Implications

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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 user’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 emoticons’ 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.

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