@InProceedings{mishra-dey-bhattacharyya:2017:Long,
  author    = {Mishra, Abhijit  and  Dey, Kuntal  and  Bhattacharyya, Pushpak},
  title     = {Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {377--387},
  abstract  = {Cognitive NLP systems- i.e., NLP systems that make use of behavioral data -
	augment traditional text-based features with cognitive features extracted from
	eye-movement patterns, EEG signals, brain-imaging etc. Such extraction of
	features is typically manual. We contend that manual extraction of features may
	not be the best way to tackle text subtleties that characteristically prevail
	in complex classification tasks like Sentiment Analysis and Sarcasm Detection,
	and that even the extraction and choice of features should be delegated to the
	learning system.  We introduce a framework to automatically extract cognitive
	features from the eye-movement/gaze data of human readers reading the text and
	use them as features along with textual features for the tasks of sentiment
	polarity and sarcasm detection. Our proposed framework is based on
	Convolutional Neural Network (CNN). The CNN learns features from both gaze and
	text and uses them to classify the input text. We test our technique on
	published sentiment and sarcasm labeled datasets, enriched with gaze
	information, to show that using a combination of automatically learned text and
	gaze features often yields better classification performance over (i)  CNN
	based systems that rely on text input alone and (ii) existing systems that rely
	on handcrafted gaze and textual features.},
  url       = {http://aclweb.org/anthology/P17-1035}
}