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


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 Computational Analysis of Affect and Emotion in Language Saif M. Mohammad, Cecilia Ovesdotter Alm 381 tutorial 178.25
2 Sentiment Analysis of Social Media Texts Saif M. Mohammad, Xiaodan Zhu 381 tutorial 170.75
3 Sentiment Analysis and Opinion Mining Bing Liu 381 survey 148.31
4 Emotions in language: Linguistic and computational approaches Diana Santos, Belinda Maia 999 tutorial 147.36
5 Affect Detection from Text – from Affect Sciences to Computational Models Alexandra Balahur 751 tutorial 141.65
6 Artificial Intelligence and Games Georgios N. Yannakakis and Julian Togelius 825 survey 139.59
7 Lexicons for Sentiment and Affect Extraction Daniel Jurafsky, James H. Martin 382 survey 138.96
8 Applications of Social Media Text Analysis Atefeh Farzindar, Diana Inkpen 957 tutorial 135.36
9 Opinion mining and sentiment analysis Bo Pang and Lillian Lee 381 survey 133.35
10 Speech and Language Processing Daniel Jurafsky, James H. Martin 133 survey 131.05
11 Sentiment Analysis and Opinion Mining Bing Liu 381 tutorial 129.11
12 Computing with Affective Lexicons Affective, Sentimental, Dan Jurafsky 382 lecture 128.66
13 Non-distributional Word Vector Representations Manaal Faruqui, Chris Dyer 999 paper 128.61
14 Multilingual Sentiment and Subjectivity Analysis Rada Mihalcea, Carmen Banea, Janyce Wiebe 381 tutorial 128.00
15 Data for Everyone library Crowdflower 961 corpus 126.71
16 Neural Network for Sentiment Analysis Yue Zhang and Duy Tin Vo 381 tutorial 125.28
17 NLP's ImageNet moment has arrived Sebastian Ruder 862 resource 124.73
18 Opinion Mining: Exploiting the Sentiment of the Crowd Diana Maynard, Adam Funk, Kalina Bontcheva 381 tutorial 124.35
19 Mathematics of Deep Learning Raja Giryes, René Vidal 713 tutorial 123.98
20 Computing with Affective Lexicons Daniel Jurafsky 382 survey 120.31
21 A Year In Computer Vision Benjamin F. Duffy, Daniel R. Flynn survey 120.12
22 Deep Unordered Composition Rivals Syntactic Methods for Text Classification Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, Hal Daumé III 999 paper 118.86
23 Natural Language Processing Jacob Eisenstein 711 survey 118.03
24 Understanding Emotions - from Keras to pyTorch Thomas Wolf 731 tutorial 117.97
25 Natural Language Processing Jacob Eisenstein 711 survey 117.50
26 Sentiment Analysis and Subjectivity Bing Liu 381 survey 115.91
27 Natural Language Processing Jacob Eisenstein 711 survey 114.33
28 Highlights of EMNLP 2017: Exciting Datasets, Return of the Clusters, and More! Sebastian Ruder 641 resource 113.05
29 Long Short-Term Memory-Networks for Machine Reading Jianpeng Cheng, Li Dong, Mirella Lapata 999 paper 112.56
30 Neural Language Model Sujith Ravi 967 tutorial 112.42
31 Learning about the world through video Moritz Mueller-Freitag 811 resource 110.71
32 Word Sense Disambiguation: A Survey Roberto Navigli 391 survey 106.86
33 Recent Developments of Content-Based Recommendation systems Giovanni Semeraro 999 tutorial 106.67
34 Introductory Guide to Artificial Intelligence Egor Dezhic 811 resource 106.36
35 Topics, Trends, and Resources in NLP Mohit Bansal 133 tutorial 106.11
36 Visualizing and Understanding Neural Models in NLP Jiwei Li, Xinlei Chen, Eduard Hovy, Dan Jurafsky 999 paper 106.10
37 Construction and Querying of Large-scale Knowledge Bases Xiang Ren, Yu Su, Xifeng Yan 974 tutorial 105.67
38 Chinese Textual Sentiment Analysis: Datasets, Resources and Tools Wei-Fan Chen and Lun-Wei Ku 381 tutorial 105.41
39 Deep Learning for Dialogue Systems Yun-Nung (Vivian) Chen, Asli Celikyilmaz, Dilek Hakkani-Tur 445 tutorial 104.65
40 Indaba at a Glance Indaba 999 survey 104.65
41 Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement Hua He, Jimmy Lin 999 paper 104.57
42 A Practitioner's Guide to Natural Language Processing (Part I)?—?Processing & Understanding Text Dipanjan (DJ) Sarker 112 resource 104.12
43 What is machine learning? Everything you need to know Nick Heath 711 resource 104.08
44 Multi-Task Learning Objectives for Natural Language Processing Author Unknown 133 resource 103.90
45 Deep Learning for Conversational AI Pei-Hao Su, Nikola MrköiÊ, Iñigo Casanueva, Ivan VuliÊ 811 tutorial 103.86
46 Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity Mohammad Taher Pilehvar, David Jurgens, Roberto Navigli 999 paper 103.73
47 KnowNet: Building a Large Net of Knowledge from the Web Montse Cuadros, German Rigau 999 paper 103.29
48 Deep Learning for Dialogue Systems Yun-Nung (Vivian) Chen, Asli Celikyilmaz, Dilek Hakkani-Tur 756 tutorial 103.10
49 Open-Domain Question Answering Mark Andrew Greenwood 411 survey 102.91
50 Deep Learning for Semantic Composition Xiaodan Zhu, Edward Grefenstette 752 tutorial 102.66