DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning
Habibeh Naderi, Behrouz Haji Soleimani, Saif M. Mohammad, Svetlana Kiritchenko, Stan Matwin · 2018
In this paper, we propose a regression system to infer the emotion intensity of a tweet.We develop a multi-aspect feature learning mechanism to capture the most discriminative semantic features of a tweet as well as the emotion information conveyed by each word in it.We combine six types of feature groups: (1) a tweet representation learned by an LSTM deep neural network on the training data, (2) a tweet representation learned by an LSTM network on a large corpus of tweets that contain emotion words (a distant supervision corpus), (3) word embeddings trained on the distant supervision corpus and averaged over all words in a tweet, (4) word and character n-grams, (5) features derived from various sentiment and emotion lexicons, and (6) other hand-crafted features.As part of the word embedding training, we also learn the distributed representations of multi-word expressions (MWEs) and negated forms of words.An SVR regressor is then trained over the full set of features.We evaluate the effectiveness of our ensemble feature sets on the SemEval-2018 Task 1 datasets and achieve a Pearson correlation of 72% on the task of tweet emotion intensity prediction.