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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
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3 NLP's ImageNet moment has arrived Sebastian Ruder 862 resource 226.20
4 Highlights of EMNLP 2017: Exciting Datasets, Return of the Clusters, and More! Sebastian Ruder 641 resource 223.45
5 Deep Learning for NLP: An Overview of Recent Trends Elvis 711 resource 219.68
6 Recent Advances in Document Summarization Jin-ge Yao, Xiaojun Wan, Jianguo Xiao 421 survey 218.84
7 Deep Learning for NLP, advancements and trends in 2017 Javier 711 resource 216.21
8 The 7 NLP Techniques That Will Change How You Communicate in the Future (Part II) James Le 133 resource 214.28
9 A Comparative Analysis of ChatBots APIs Author Unknown 921 resource 208.57
10 Discriminative Syntax-based Word Ordering for Text Generation Yue Zhang, Stephen Clark 999 paper 207.46
11 Negated bio-events: analysis and identification Raheel Nawaz, Paul Thompson, Sophia Ananiadou 999 paper 206.19
12 A Gentle Introduction to Machine Learning Author Unknown 711 tutorial 206.16
13 NLP’s generalization problem, and how researchers are tackling it Ana Marasovic 711 resource 203.50
14 Transfer Learning - Machine Learnings Next Frontier Sebastian Ruder 978 tutorial 201.75
15 Understanding Convolutional Neural Networks for NLP Denny Britz 744 tutorial 199.10
16 The Definitive Guide to Natural Language Processing Javier Couto 133 tutorial 197.52
17 Deep Learning in NLP Vered Shwartz 711 resource 195.85
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19 Introduction to Natural Language Processing (NLP) 2016 Matt Kiser 133 tutorial 193.90
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21 Machine Learning for Humans Vishal Maini, Samer Sabri 134 tutorial 191.02
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24 An Overview of Multi-Task Learning in Deep Neural Networks Sebastian Ruder 829 tutorial 189.58
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29 Recurrent Neural Networks Stephen Grossberg 741 paper 184.05
30 DEEP LEARNING FOR CHATBOTS, PART 1 - INTRODUCTION Denny Britz 445 tutorial 181.19
31 Learning AI if You Suck at Math?—?P5?—?Deep Learning and Convolutional Neural Nets in Plain English! Daniel Jeffries 811 tutorial 181.09
32 12 Frequently Asked Questions on Deep Learning (with their answers)! Analytics Vidhya Content Team 711 resource 180.48
33 An end to end implementation of a Machine Learning pipeline Spandan Madan 107 tutorial 180.45
35 30 Amazing Applications of Deep Learning Yaron Hadad 711 resource 179.65
36 How do we capture structure in relational data? Matthew Das Sarma 711 resource 179.62
37 Recurrent Neural Networks Tutorial, Part 1 - Introduction to RNNs Denny Britz 741 tutorial 179.15
38 Applied Deep Learning - Part 4: Convolutional Neural Networks Arden Dertat 744 resource 176.75
39 A Practitioner's Guide to Natural Language Processing (Part I)?—?Processing & Understanding Text Dipanjan (DJ) Sarker 112 resource 176.72
40 Gimli: open source and high-performance biomedical name recognition David Campos, Sergio Matos, Jose Oliveira 999 paper 175.19
41 Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience Ismael Rafols, Martin Meyer 999 paper 175.07
42 Deep Learning for NLP Best Practices Sebastian Ruder 713 tutorial 174.80
43 A Microsoft CNTK tutorial in Python – build a neural network Andy Thomas 711 tutorial 174.14
44 Rohan #2: Artificial intelligence, ?Progress/?Time Rohan Kapur 811 tutorial 173.97
45 Ideas on interpreting machine learning Patrick Hall, Wen Phan, SriSatish Ambati 134 tutorial 173.58
46 State-of-the-art neural coreference resolution for chatbots Thomas Wolf 756 tutorial 173.38
47 Ultimate Guide to Understand & Implement Natural Language Processing (with codes in Python) Shivam Bansal 131 tutorial 172.77
48 Learning AI if You Suck at Math?—?P7?—?The Magic of Natural Language Processing Daniel Jeffries 133 tutorial 172.74
49 Transfer Learning: Leverage Insights from Big Data Lars Hulstaert 753 resource 172.50
50 Recurrent Neural Networks Tutorial, Part 2 - Implementing a RNN with Python, Numpy, and Theano Denny Britz 741 tutorial 169.76
51 RNNs in Tensorflow, a Practical Guide and Undocumented Features Denny Britz 741 tutorial 169.71
52 A survey of cross-lingual embedding models Sebastian Ruder 721 tutorial 168.40
53 The Unreasonable Effectiveness of Recurrent Neural Networks Andrej Karpathy 741 survey 168.05
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59 Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano Denny Britz 742 tutorial 166.01
60 Twitter Sentiment Analysis Using Combined LSTM-CNN Models Author Unknown 381 resource 165.06
61 Natural Language Processing (NLP) for Computational Social Science Cristian Danescu-Niculescu-Mizil, Lillian Lee 133 tutorial 163.79
62 Multi-Task Learning Objectives for Natural Language Processing Author Unknown 133 resource 163.73
63 The Building Blocks of Interpretability Chris Olah 614 resource 163.51
64 An Intuitive Guide to Linear Algebra Kalid Azad 121 tutorial 162.43
65 Introduction to Word2Vec Skymind 721 tutorial 161.68
66 Rohan & Lenny #3: Recurrent Neural Networks & LSTMs Rohan Kapur 741 tutorial 161.61
67 Learning to Segment Piotr Dollar 862 tutorial 161.21
68 Deep Learning 2: Part 1 Lesson 4 Hiromi Suenaga 711 resource 161.13
69 Architecture of Convolutional Neural Networks (CNNs) demystified Dishashree Gupta 744 tutorial 160.83
70 Introduction to Learning to Trade with Reinforcement Learning Denny Britz 857 resource 160.79
71 A Beginners Guide to Deep Learning Kumar Shridhar 711 tutorial 160.59
72 A curated list of data science, analysis and visualization tools QuantMind 134 resource 160.19
73 Summaries and notes on Deep Learning research papers Denny Britz 713 resource 159.83
74 Text Classifier Algorithms in Machine Learning Roman Trusov 542 tutorial 159.76
75 Natural Language Processing for Beginners: Using TextBlob Shubham Jain 731 resource 159.63
76 From Natural Language Processing to Ar4ficial Intelligence Jonathan Mugan 133 tutorial 159.57
77 An Overview of Proxy-label Approaches for Semi-supervised Learning Sebastian Ruder 581 resource 159.19
78 What is the difference between convolutional neural networks, restricted Boltzmann machines, and auto-encoders? ffriend 744 tutorial 158.94
79 Making computers explain themselves Larry Hardesty 713 resource 158.68
80 New approaches to Deep Networks:Capsules (Hinton), HTM (Numenta), Sparsey (Neurithmic Systems) and RCN (Vicarious) Gideon Kowadlo 731 resource 158.36
81 Prodigy: A new tool for radically efficient machine teaching Matthew Honnibal, Ines Montani 134 resource 158.05
82 Getting Started with Sentiment Analysis bogdani 381 resource 157.78
83 Machine Learning Glossary Author Unknown 107 resource 157.76
84 Simple, Strong Deep-Learning Baselines for NLP in several frameworks Dan Pressel 713 library 157.53
85 What is machine learning? Everything you need to know Nick Heath 711 resource 157.48
86 TensorFlow RNN Tutorial Matt Mollison 731 resource 157.45
87 Neural Text Embeddings for IR Bhaskar Mitra, Nick Craswell 721 tutorial 157.05
88 Comprehensive Guide on t-SNE algorithm with implementation in R & Python SAURABH.JAJU2 341 tutorial 156.92
89 A social network's changing statistical properties and the quality of human innovation Brian Uzzi 999 paper 156.88
90 A Beginner's guide to Recurrent Networks and LSTMs Skymind 742 tutorial 156.58
91 Keras and Convolutional Neural Networks (CNNs) Adrian Rosebrock 744 resource 156.52
92 Machine Learning Basics: a Guide for the Perplexed Will Gannon 134 resource 156.25