IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning
Maximilian Köper, Evgeny Kim, Roman Klinger · 2017
Our submission to the WASSA-2017 shared task on the prediction of emotion intensity in tweets is a supervised learning method with extended lexicons of affective norms.We combine three main information sources in a random forrest regressor, namely (1), manually created resources, (2) automatically extended lexicons, and (3) the output of a neural network (CNN-LSTM) for sentence regression.All three feature sets perform similarly well in isolation (≈ .67 macro average Pearson correlation).The combination achieves .72 on the official test set (ranked 2nd out of 22 participants).Our analysis reveals that performance is increased by providing cross-emotional intensity predictions.The automatic extension of lexicon features benefit from domain specific embeddings.Complementary ratings for affective norms increase the impact of lexicon features.