SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets
This work addresses sentiment and emotion analysis in social media data, representing an incremental advance in domain-specific applications.
The paper tackled the problem of affect detection in tweets for SemEval-2018 tasks, achieving first place out of 75 teams by using domain adaptation and ensemble methods, with performance improvements over the baseline ranging from 49.2% to 76.4%.
The paper describes the best performing system for the SemEval-2018 Affect in Tweets (English) sub-tasks. The system focuses on the ordinal classification and regression sub-tasks for valence and emotion. For ordinal classification valence is classified into 7 different classes ranging from -3 to 3 whereas emotion is classified into 4 different classes 0 to 3 separately for each emotion namely anger, fear, joy and sadness. The regression sub-tasks estimate the intensity of valence and each emotion. The system performs domain adaptation of 4 different models and creates an ensemble to give the final prediction. The proposed system achieved 1st position out of 75 teams which participated in the fore-mentioned sub-tasks. We outperform the baseline model by margins ranging from 49.2% to 76.4%, thus, pushing the state-of-the-art significantly.