@InProceedings{proisl-EtAl:2018:WASSA2018,
  author    = {Proisl, Thomas  and  Heinrich, Philipp  and  Kabashi, Besim  and  Evert, Stefan},
  title     = {EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit Emotions},
  booktitle = {Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {235--242},
  abstract  = {EmotiKLUE is a submission to the Implicit Emotion Shared Task. It is a deep learning system that combines independent representations of the left and right contexts of the emotion word with the topic distribution of an LDA topic model. EmotiKLUE achieves a macro average F1 score of 67.13%, significantly outperforming the baseline produced by a simple ML classifier. Further enhancements after the evaluation period lead to an improved F1 score of 68.10%.},
  url       = {http://aclweb.org/anthology/W18-6234}
}