  The WASSA 2017 EmoInt shared task has the goal to predict emotion intensity
values of tweet messages. Given the text of a tweet and its emotion category
(anger, joy, fear, and sadness), the participants were asked to build a system
that assigns emotion intensity values. Emotion intensity estimation is a
challenging problem given the short length of the tweets, the noisy structure
of the text and the lack of annotated data. To solve this problem, we developed
an ensemble of two neural models, processing input on the character. and
word-level with a lexicon-driven system. The correlation scores across all four
emotions are averaged to determine the bottom-line competition metric, and our
system ranks place forth in full intensity range and third in 0.5-1 range of
intensity among 23 systems at the time of writing (June 2017).
