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WASSA-2017 Shared Task on Emotion Intensity
Saif Mohammad
|
Felipe Bravo-Marquez
|
Paper Details:
Month: September
Year: 2017
Location: Copenhagen, Denmark
Venue:
WASSA |
WS |
Citations
URL
How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs
Devang Kulshreshtha
|
Pranav Goel
|
Anil Kumar Singh
|
Modeling Empathy and Distress in Reaction to News Stories
Sven Buechel
|
Anneke Buffone
|
Barry Slaff
|
Lyle Ungar
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João Sedoc
|
Modelling Representation Noise in Emotion Analysis using Gaussian Processes
Daniel Beck
|
Word Affect Intensities
Saif Mohammad
|
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories
Saif Mohammad
|
Svetlana Kiritchenko
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A Comparison Of Emotion Annotation Schemes And A New Annotated Data Set
Ian Wood
|
John P. McCrae
|
Vladimir Andryushechkin
|
Paul Buitelaar
|
WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art
Saif Mohammad
|
Svetlana Kiritchenko
|
Complex and Precise Movie and Book Annotations in French Language for Aspect Based Sentiment Analysis
Stefania Pecore
|
Jeanne Villaneau
|
Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem
Sven Buechel
|
Udo Hahn
|
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words
Saif Mohammad
|
Emotion Intensities in Tweets
Saif Mohammad
|
Felipe Bravo-Marquez
|
SemEval-2018 Task 1: Affect in Tweets
Saif Mohammad
|
Felipe Bravo-Marquez
|
Mohammad Salameh
|
Svetlana Kiritchenko
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LT3 at SemEval-2018 Task 1: A classifier chain to detect emotions in tweets
Luna De Bruyne
|
Orphée De Clercq
|
Véronique Hoste
|
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets
Mostafa Abdou
|
Artur Kulmizev
|
Joan Ginés i Ametllé
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deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets
Zi-Yuan Gao
|
Chia-Ping Chen
|
Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems
Svetlana Kiritchenko
|
Saif Mohammad
|
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus
Hendrik Schuff
|
Jeremy Barnes
|
Julian Mohme
|
Sebastian Padó
|
Roman Klinger
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IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning
Maximilian Köper
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Evgeny Kim
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Roman Klinger
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Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets
Pranav Goel
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Devang Kulshreshtha
|
Prayas Jain
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Kaushal Kumar Shukla
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GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection
Egor Lakomkin
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Chandrakant Bothe
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Stefan Wermter
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NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity
Vladimir Andryushechkin
|
Ian Wood
|
James O’ Neill
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PLN-PUCRS at EmoInt-2017: Psycholinguistic features for emotion intensity prediction in tweets
Henrique Santos
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Renata Vieira
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YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction
You Zhang
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Hang Yuan
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Jin Wang
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Xuejie Zhang
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IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features
Md Shad Akhtar
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Palaash Sawant
|
Asif Ekbal
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Jyoti Pawar
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Pushpak Bhattacharyya
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NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets
Sreekanth Madisetty
|
Maunendra Sankar Desarkar
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EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity
Edison Marrese-Taylor
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Yutaka Matsuo
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YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model
Yuanye He
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Liang-Chih Yu
|
K. Robert Lai
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Weiyi Liu
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DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method
Song Jiang
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Xiaotian Han
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LIPN-UAM at EmoInt-2017:Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity Determination
Davide Buscaldi
|
Belem Priego
|
IEST: WASSA-2018 Implicit Emotions Shared Task
Roman Klinger
|
Orphée De Clercq
|
Saif Mohammad
|
Alexandra Balahur
|
IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations
Jorge Balazs
|
Edison Marrese-Taylor
|
Yutaka Matsuo
|
http://saifmohammad.com/WebPages/EmotionIntensity-
https://competitions.codalab.org/competitions/16380
https://github.com/felipebravom/AffectiveTweets
http://saifmohammad.com/WebPages/BestWorst.html
https://competitions.codalab.org/competitions/16380
http://saifmohammad.com/WebPages/lexicons.html
https://github.com/felipebravom/AffectiveTweets
http://www.csie.ntu.edu.tw/cjlin/liblinear/
http://saifmohammad.com/WebPages/AffectIntensity.htm
http://alt.qcri.org/semeval2018/
http://aclweb.org/anthology/W16-6208
https://doi.org/10.1145/1656274.1656278
https://doi.org/10.1111/coin.12024
http://saifmohammad.com/WebPages/EmotionIntensity-SharedTask.html
https://code.google.com/archive/p/word2vec/
Field Of Study
Linguistic Trends
Embeddings
Task
Tagging
Named Entity Recognition
Sentiment Analysis
Emotion Detection
Approach
Deep Learning
Language
English
Arabic
Dataset
Social Media
Twitter
Blogs
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