IMS at EmoInt-2017, Code and Resources
Maximilian Köper, Evgeny Kim, Roman Klinger
This page contains the code and resources used by our system submission for the WASSA Emotion Intensity Shared Task (EmoInt). Our system (IMS) scored 2nd out of 22. The system makes use of affective norms, automatically extended norms and deep learning.
Full code and walkthrough example can be found here:
Automatically Extended Norms of Affective Ratings and Emotion Lexicons for ~1.6m English Words
can be downloaded here (65MB) or by contacting one of the authors.
The file contains ratings for:
The file is tab-seperated and the first column contains the word. Ratings range from [0,10].
Here are some (random) examples
Word AbsConc Arousal Valency Dominance Anger #hate 1.4 6.697 0.693 2.514 5.742 babygiggles 6.128 3.618 9.544 6.772 3.613 pizza 8.261 4.205 7.382 6.203 1.602 #anxiety 1.615 4.849 1.823 1.607 3.796 Word Anticipation Disgust Fear Happiness Joy #hate 2.666 5.83 2.909 0.999 1.197 babygiggles 2.814 2.503 1.946 10 7.067 pizza 1.584 2.375 1.462 7.473 0.979 #anxiety 2.402 3.308 9.484 2.295 2.945 Word Sadness Surprise Trust #hate 1.293 3.273 2.793 babygiggles 3.443 6.379 2.045 pizza 0.58 1.391 0.904 #anxiety 3.858 5.048 3.723
Please cite the official paper [BIB]
Maximilian Köper, Evgeny Kim, and Roman Klinger. IMS at EmoInt-2017: Emotion intensity prediction with affective norms, automatically extended resources and deep learning. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Copenhagen, Denmark, 2017. Workshop at Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics.
Please consider to cite in addition the publication reference for the underyling training data (see our paper for the data used per rating type).