DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning

Habibeh Naderi, Behrouz Haji Soleimani, Saif Mohammad, Svetlana Kiritchenko, Stan Matwin


Abstract
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.
Anthology ID:
S18-1045
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
305–312
Language:
URL:
https://www.aclweb.org/anthology/S18-1045
DOI:
10.18653/v1/S18-1045
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1045.pdf