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Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network


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.


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# Title Author Topic Medium Score
1 Computational Analysis of Affect and Emotion in Language Saif M. Mohammad, Cecilia Ovesdotter Alm 381 tutorial 287.74
2 Neural Network Methods for Natural Language Processing Yoav Goldberg 713 survey 248.16
3 Sentiment Analysis and Opinion Mining Bing Liu 381 survey 246.72
4 Affect Detection from Text – from Affect Sciences to Computational Models Alexandra Balahur 751 tutorial 243.21
5 Improving sentence compression by learning to predict gaze Sigrid Klerke, Yoav Goldberg, Anders Søgaard 426 paper 241.93
6 Artificial Intelligence and Games Georgios N. Yannakakis and Julian Togelius 825 survey 240.97
7 Opinion mining and sentiment analysis Bo Pang and Lillian Lee 381 survey 231.89
8 Opinion Mining: Exploiting the Sentiment of the Crowd Diana Maynard, Adam Funk, Kalina Bontcheva 381 tutorial 227.74
9 Deep Learning for NLP: An Overview of Recent Trends Elvis 711 resource 226.20
10 Topics, Trends, and Resources in NLP Mohit Bansal 133 tutorial 222.33
11 A Year In Computer Vision Benjamin F. Duffy, Daniel R. Flynn survey 218.95
12 Speech and Language Processing Daniel Jurafsky, James H. Martin 133 survey 217.79
13 Modality and Negation in Natural Language Processing Roser Morante 112 tutorial 216.76
14 Sentiment Analysis and Opinion Mining Bing Liu 381 tutorial 215.90
15 Scholarly Data Mining: Making Sense of Scientific Literature Horacio Saggion & Francesco Ronzano 974 tutorial 215.30
16 Scholarly Data Mining: Making Sense of Scientific Literature Horacio Saggion, Francesco Ronzano 974 tutorial 215.30
17 On the Origin of Deep Learning Haohan Wang, Bhiksha Raj survey 214.27
18 Sentiment Analysis and Subjectivity Bing Liu 381 survey 214.23
19 Natural Language Processing for Intelligent Access to Scientific Information Francesco Ronzano, Horacio Saggion 232 tutorial 213.44
20 Capturing Dependency Syntax with "Deep" Sequential Models Yoav Goldberg 711 tutorial 211.69
21 Neural Information Retrieval: At the End of the Early Years Kezban Dilek Onal, Ye Zhang, Ismail Sengor Altingovde, Md Mustafizu... 713 resource 211.12
22 Sentiment Analysis of Social Media Texts Saif M. Mohammad, Xiaodan Zhu 381 tutorial 208.36
23 Automatic Summarization Ani Nenkova and Kathleen McKeown 421 survey 208.14
24 A Primer on Neural Network Models for Natural Language Processing Yoav Goldberg 711 survey 207.21
25 Applications of Social Media Text Analysis Atefeh Farzindar, Diana Inkpen 957 tutorial 207.10
26 A Long Short-Term Memory Framework for Predicting Humor in Dialogues Dario Bertero, Pascale Fung 999 paper 206.28
27 Cross-Lingual Word Representations: Induction and Evaluation Ivan Vulic, Anders Sogaard, Manaal Faruqui 999 survey 205.73
28 Extracting World and Linguistic Knowledge from Wikipedia Simone Paolo Ponzetto, Michael Strube 232 tutorial 205.34
29 Deep Learning for NLP, advancements and trends in 2017 Javier 711 resource 204.99
30 Natural Language Processing Jacob Eisenstein 711 survey 204.97
31 Machine Learning for NLP Charu C. Aggarwal 711 survey 203.71
32 Natural Language Processing Jacob Eisenstein 711 survey 202.90
33 The 7 NLP Techniques That Will Change How You Communicate in the Future (Part II) James Le 133 resource 202.90
34 Tackling the Limits of Deep Learning for NLP Richard Socher resource 199.81
35 Multilingual Sentiment and Subjectivity Analysis Rada Mihalcea, Carmen Banea, Janyce Wiebe 381 tutorial 199.74
36 Deep Reinforcement Learning: An Overview Yuxi Li 857 resource 199.19
37 Sentiment Analysis in Practice Yongzheng (Tiger) Zhang, Dan Shen, Catherine Baudin 381 tutorial 198.98
38 Deep Learning in Neural Networks: An Overview J?rgen Schmidhuber survey 198.58
39 A tutorial survey of architectures, algorithms, and applications for deep learning Li Deng survey 198.48
40 Negated bio-events: analysis and identification Raheel Nawaz, Paul Thompson, Sophia Ananiadou 999 paper 196.74
41 Natural Language Processing Jacob Eisenstein 711 survey 196.73
42 Lexicons for Sentiment and Affect Extraction Daniel Jurafsky, James H. Martin 382 survey 195.70
43 Long Short-Term Memory-Networks for Machine Reading Jianpeng Cheng, Li Dong, Mirella Lapata 999 paper 195.68
44 Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents Rui Zhang, Honglak Lee, Dragomir R. Radev 999 paper 195.26
45 Opinion Mining & Summarization - Sentiment Analysis Bing Liu 381 tutorial 195.18
46 Named Entity Recognition and Classification David Nadeau, Satoshi Sekine 232 survey 194.87
47 An Introduction to Automatic Text Simplification Horacio Saggion 426 tutorial 194.37
48 Understanding Convolutional Neural Networks for NLP Denny Britz 744 tutorial 194.32
49 Information Extraction Sunita Sarawagi 232 survey 194.29
50 NLP's ImageNet moment has arrived Sebastian Ruder 862 resource 194.25