@InProceedings{duppada-jain-hiray:2018:S18-1,
  author    = {Duppada, Venkatesh  and  Jain, Royal  and  Hiray, Sushant},
  title     = {SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {18--23},
  abstract  = {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 1stposition 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.},
  url       = {http://www.aclweb.org/anthology/S18-1002}
}