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Title:

EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks

Abstract:

Accurate detection of emotion from natural language has applications ranging from building emotional chatbots to better understanding individuals and their lives. However, progress on emotion detection has been hampered by the absence of large labeled datasets. In this work, we build a very large dataset for fine-grained emotions and develop deep learning models on it. We achieve a new state-of-the-art on 24 fine-grained types of emotions (with an average accuracy of 87.58%). We also extend the task beyond emotion types to model Robert Plutchik’s 8 primary emotion dimensions, acquiring a superior accuracy of 95.68%.

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# Title Author Topic Medium Score
1 Data for Everyone library Crowdflower 961 corpus 142.06
2 Highlights of EMNLP 2017: Exciting Datasets, Return of the Clusters, and More! Sebastian Ruder 641 resource 138.74
3 Codra: A Novel Discriminative Framework for Rhetorical Analysis Shafiq Joty, Giuseppe Carenini, Raymond T. Ng 999 paper 120.07
4 A Practitioner's Guide to Natural Language Processing (Part I)?—?Processing & Understanding Text Dipanjan (DJ) Sarker 112 resource 115.95
5 Understanding Emotions - from Keras to pyTorch Thomas Wolf 731 tutorial 114.98
6 Machine Learning for Humans Vishal Maini, Samer Sabri 134 tutorial 113.11
7 Introductory Guide to Artificial Intelligence Egor Dezhic 811 resource 112.74
8 Neural Language Model Sujith Ravi 967 tutorial 111.75
9 Learning about the world through video Moritz Mueller-Freitag 811 resource 110.79
10 An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship J. E. Hirsch 999 paper 110.59
11 What is machine learning? Everything you need to know Nick Heath 711 resource 110.37
12 Similarity-driven Semantic Role Induction via Graph Partitioning Joel Lang, Mirella Lapata 999 paper 107.87
13 State-of-the-art neural coreference resolution for chatbots Thomas Wolf 756 tutorial 106.56
14 Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience Ismael Rafols, Martin Meyer 999 paper 105.09
15 TorchMoji Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan and Sune Lehmann 811 library 103.51
16 The Future (and Present) of Artificial Intelligence AMA Various Authors 811 resource 102.93
17 The data that transformed AI research—and possibly the world Dave Gershgorn 107 resource 102.52
18 Transfer Learning - Machine Learnings Next Frontier Sebastian Ruder 978 tutorial 101.77
19 PyTorch-GAN Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, Eli Shechtman 731 library 98.63
20 DEEP LEARNING FOR CHATBOTS, PART 1 - INTRODUCTION Denny Britz 445 tutorial 98.24
21 A survey of transfer learning Karl Weiss, Taghi M. Khoshgoftaar and DingDing Wang 978 resource 96.53
22 Recurrent Neural Networks Tutorial, Part 2 - Implementing a RNN with Python, Numpy, and Theano Denny Britz 741 tutorial 96.09
23 Deep Learning, The Curse of Dimensionality, and Autoencoders Nikhil Buduma 711 tutorial 95.91
24 Explain yourself, machine. Producing simple text descriptions for AI interpretability. Luke Oakden-Rayner 811 resource 95.86
25 Recent Advances in Document Summarization Jin-ge Yao, Xiaojun Wan, Jianguo Xiao 421 survey 95.71
26 spaCy 101: Everything you need to know Author Unknown 731 resource 95.30
27 Multi-Task Learning Objectives for Natural Language Processing Author Unknown 133 resource 95.01
28 Neural Information Retrieval: At the End of the Early Years Kezban Dilek Onal, Ye Zhang, Ismail Sengor Altingovde, Md Mustafizur Rahman, Pinar Karagoz, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, Aaron Angert, Edward Banner, Vivek K 713 resource 94.17
29 DEEP LEARNING FOR CHATBOTS, PART 2 - IMPLEMENTING A RETRIEVAL-BASED MODEL IN TENSORFLOW Denny Britz 445 tutorial 94.09
30 Chatsbots with Machine Learning: Building Neural Conversational Agents Dmitry Persiyanov 999 resource 94.06
31 Negated bio-events: analysis and identification Raheel Nawaz, Paul Thompson, Sophia Ananiadou 999 paper 93.99
32 Quora Duplicate Questions Corpus Quora 151 corpus 93.79
33 spaCy: Named Entities Spacy 232 resource 93.72
34 Rohan #1: Logistic regression case study?—?diagnosing cancer Rohan Kapur 516 tutorial 93.67
35 Ideas on interpreting machine learning Patrick Hall, Wen Phan, SriSatish Ambati 134 tutorial 92.99
36 Rohan #2: Artificial intelligence, ?Progress/?Time Rohan Kapur 811 tutorial 92.56
37 Recurrent Neural Networks Stephen Grossberg 741 paper 92.51
38 Machine Learning Workflows in Python from Scratch Part 1: Data Preparation Matthew Mayo 107 tutorial 91.56
39 AI in 2018 for researchers Alex Honchar 811 resource 90.71
40 Learning when to skim and when to read Alexander Rosenberg Johansen, Bryan McCann, James Bradbury, Richard Socher 713 tutorial 90.53
41 Introduction to Visual Question Answering: Datasets, Approaches and Evaluation Javier Couto 411 resource 89.85
42 An Overview of Multi-Task Learning in Deep Neural Networks Sebastian Ruder 829 tutorial 89.82
43 The h’-Index, Effectively Improving the h-Index Based on the Citation Distribution Chun-Ting Zhang 999 paper 89.79
44 Natural Language Processing Key Terms, Explained Matthew Mayo 133 tutorial 89.32
45 Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano Denny Britz 742 tutorial 89.10
46 Theano Tutorial Marek Rei 731 tutorial 87.90
47 cs231n notes: Classification Andrej Karpathy 511 tutorial 87.87
48 Discriminative Syntax-based Word Ordering for Text Generation Yue Zhang, Stephen Clark 999 paper 87.82
49 Natural Language Processing in Artificial Intelligence is almost human-level accurate. Worse yet, it gets smart! Rafal 133 tutorial 87.74
50 Natural Language Processing for Beginners: Using TextBlob Shubham Jain 731 resource 87.57
51 25 Open Datasets for Deep Learning Every Data Scientist Must Work With Pranav Dar 731 resource 86.96
52 Deep Learning for NLP, advancements and trends in 2017 Javier 711 resource 86.64
53 Recurrent Neural Networks Tutorial, Part 1 - Introduction to RNNs Denny Britz 741 tutorial 86.26
54 Must Know Tips/Tricks in Deep Neural Networks Xiu-Shen Wei 713 tutorial 85.93
55 Sentiment Analysis 101 Scott Sims 381 tutorial 85.88
56 12 Frequently Asked Questions on Deep Learning (with their answers)! Analytics Vidhya Content Team 711 resource 85.74
57 Generative Models Andrej Karpathy, Pieter Abbeel, Greg Brockman, Peter Chen, Vicki Cheung, Rocky Duan, Ian Goodfellow, Durk Kingma, Jonathan Ho, Rein Houthooft, Tim Salimans, John Schulman, Ilya Sutskever, Wojciech Zar 756 resource 85.50
58 Requests for Research Sebastian Ruder 921 resource 85.43
59 Yoshua Bengio on intelligent machines Yoshua Bengio 811 tutorial 85.19
60 ImageNet: VGGNet, ResNet, Inception, and Xception with Keras Adrian Rosebrock 741 resource 85.07
61 The 8 Neural Network Architectures Machine Learning Researchers Need to Learn Nand Kishor 711 resource 84.97
62 Do Altmetrics Work? Twitter and Ten Other Social Web Services Mike Thelwall, Stefanie Haustein, Vincent Larivière, Cassidy R. Sugimoto 999 paper 84.73
63 A large-scale community structure analysis in Facebook Emilio Ferrara 999 paper 84.71
64 100+ Interesting Data Sets for Statistics Robb Seaton 999 corpus 84.62
65 Open Machine Learning Course. Topic 5. Bagging and Random Forest Yury Kashnitskiy 711 resource 84.41
66 Deep text-pair classification with Quora’s 2017 question dataset Matthew Honnibal 133 tutorial 84.22
67 Understanding Convolutional Neural Networks for NLP Denny Britz 744 tutorial 84.09
68 K-Means & Other Clustering Algorithms: A Quick Intro with Python Nikos Koufos 571 tutorial 83.58
69 Improving Language Understanding with Unsupervised Learning Alec Radford 581 resource 83.50
70 The Definitive Guide to Natural Language Processing Javier Couto 133 tutorial 83.47
71 Building a Question-Answering System from Scratch— Part 1 Alvira Swalin 411 resource 83.38
72 Interpretability via attentional and memory-based interfaces, using TensorFlow Goku Mohandas 731 tutorial 83.33
73 Introducing state of the art text classification with universal language models Jeremy Howard, Sebastian Ruder 542 resource 83.08
74 Simple Audio Recognition Author Unknown 944 tutorial 83.07
75 Learning AI if You Suck at Math?—?P7?—?The Magic of Natural Language Processing Daniel Jeffries 133 tutorial 83.05
76 Summaries and notes on Deep Learning research papers Denny Britz 713 resource 83.02
77 Artificial intelligence Ted Playlist TED 811 resource 82.89
78 Deep Learning Summer School Invited Speakers with slides CIFAR 713 resource 82.85
79 Prodigy: A new tool for radically efficient machine teaching Matthew Honnibal, Ines Montani 134 resource 82.72
80 Tombones Computer Vision Blog Tomasz Malisiewicz 958 resource 82.63
81 Google DeepMind's AlphaGo: How it works Christopher Burger 711 tutorial 82.60
82 Neural Text Embeddings for IR Bhaskar Mitra, Nick Craswell 721 tutorial 82.37
83 19 Data Science Tools for people who aren’t so good at Programming Aarshay Jain 107 tutorial 82.36
84 The Stanford Question Answering Dataset Pranav Rajpurkar 411 resource 82.32
85 The Unreasonable Ineffectiveness of Deep Learning in NLU Suman Deb Roy 713 tutorial 82.16
86 Introduction to Word2Vec Skymind 721 tutorial 81.37
87 End-to-end speech Anthony Ndirango 863 resource 81.36
88 The history and meaning of the journal impact factor Eugene Garfield 999 paper 81.30
89 Neural Networks Tutorial – A Pathway to Deep Learning Andy Thomas 711 tutorial 81.08
90 SippyCup Unit 2: Travel queries Bill MacCartney 365 tutorial 81.00
91 Transfer learning & The art of using Pre-trained Models in Deep Learning Dishashree Gupta 711 resource 80.96