UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets
Pavel P{\v{r}}ib{\'a}{\v{n}}, Tom{\'a}{\v{s}} Hercig, Ladislav Lenc
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This paper describes our system created for the SemEval-2018 Task 1: Affect in Tweets (AIT-2018). We participated in both the regression and the ordinal classification subtasks for emotion intensity detection in English, Arabic, and Spanish. For the regression subtask we use the AffectiveTweets system with added features using various word embeddings, lexicons, and LDA. For the ordinal classification we additionally use our Brainy system with features using parse tree, POS tags, and morphological features. The most beneficial features apart from word and character n-grams include word embeddings, POS count and morphological features.