PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and #hashtags
This work addresses emotion analysis in social media for NLP applications, but it is incremental as it builds on existing methods with new data sources.
The paper tackled emotion detection in tweets by modeling sentence and word-level representations using distantly labeled corpora with emojis and hashtags, achieving Top3 rankings in all subtasks of SemEval-2018 Task 1.
This paper describes our system that has been submitted to SemEval-2018 Task 1: Affect in Tweets (AIT) to solve five subtasks. We focus on modeling both sentence and word level representations of emotion inside texts through large distantly labeled corpora with emojis and hashtags. We transfer the emotional knowledge by exploiting neural network models as feature extractors and use these representations for traditional machine learning models such as support vector regression (SVR) and logistic regression to solve the competition tasks. Our system is placed among the Top3 for all subtasks we participated.