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ORCID8th WASSA@EMNLP 2017: Copenhagen, Denmark
- Alexandra Balahur, Saif M. Mohammad, Erik van der Goot:
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA@EMNLP 2017, Copenhagen, Denmark, September 8, 2017. Association for Computational Linguistics 2017, ISBN 978-1-945626-95-1 - Aditya Joshi:
Detecting Sarcasm Using Different Forms Of Incongruity. 1 - Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde:
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets. 2-12 - Hendrik Schuff, Jeremy Barnes, Julian Mohme, Sebastian Padó
, Roman Klinger:
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus. 13-23 - Matthias Hartung, Roman Klinger, Franziska Schmidtke, Lars Vogel:
Ranking Right-Wing Extremist Social Media Profiles by Similarity to Democratic and Extremist Groups. 24-33 - Saif M. Mohammad, Felipe Bravo-Marquez:
WASSA-2017 Shared Task on Emotion Intensity. 34-49 - Maximilian Köper, Evgeny Kim, Roman Klinger:
IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning. 50-57 - Prayas Jain, Pranav Goel, Devang Kulshreshtha, Kaushal Kumar Shukla:
Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets. 58-65 - Iryna Gurevych:
Latest News in Computational Argumentation: Surfing on the Deep Learning Wave, Scuba Diving in the Abyss of Fundamental Questions. 66 - David Vilares, Marcos García, Miguel A. Alonso, Carlos Gómez-Rodríguez:
Towards Syntactic Iberian Polarity Classification. 67-73 - Filip Boltuzic, Jan Snajder:
Toward Stance Classification Based on Claim Microstructures. 74-80 - JiaQi Wu, Marilyn A. Walker, Pranav Anand, Steve Whittaker:
Linguistic Reflexes of Well-Being and Happiness in Echo. 81-91 - Viktor Pekar, Jane M. Binner:
Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media. 92-101 - Edison Marrese-Taylor, Jorge A. Balazs, Yutaka Matsuo:
Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN. 102-111 - Alexandra Balahur:
Understanding human values and their emotional effect. 112 - Rebeca Padilla López, Fabienne Cap:
Did you ever read about Frogs drinking Coffee? Investigating the Compositionality of Multi-Emoji Expressions. 113-117 - Giulia Donato, Patrizia Paggio:
Investigating Redundancy in Emoji Use: Study on a Twitter Based Corpus. 118-126 - Kishaloy Halder, Lahari Poddar, Min-Yen Kan:
Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach. 127-135 - Orphée De Clercq, Els Lefever, Gilles Jacobs, Tijl Carpels, Véronique Hoste:
Towards an integrated pipeline for aspect-based sentiment analysis in various domains. 136-142 - Gaurav Mohanty, Abishek Kannan, Radhika Mamidi:
Building a SentiWordNet for Odia. 143-148 - Bonggun Shin, Timothy Lee, Jinho D. Choi:
Lexicon Integrated CNN Models with Attention for Sentiment Analysis. 149-158 - Leila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Explaining Recurrent Neural Network Predictions in Sentiment Analysis. 159-168 - Egor Lakomkin, Chandrakant Bothe, Stefan Wermter:
GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection. 169-174 - Vladimir Andryushechkin, Ian D. Wood, James O'Neill:
NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity. 175-179 - Athanasios Giannakopoulos, Claudiu Musat, Andreea Hossmann, Michael Baeriswyl:
Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets. 180-188 - Henrique D. P. dos Santos, Renata Vieira:
PLN-PUCRS at EmoInt-2017: Psycholinguistic features for emotion intensity prediction in tweets. 189-192 - Hardik Meisheri, Rupsa Saha, Priyanka Sinha, Lipika Dey:
Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets. 193-199 - You Zhang, Hang Yuan, Jin Wang, Xuejie Zhang:
YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction. 200-204 - Venkatesh Duppada, Sushant Hiray:
Seernet at EmoInt-2017: Tweet Emotion Intensity Estimator. 205-211 - Md. Shad Akhtar, Palaash Sawant, Asif Ekbal, Jyoti D. Pawar, Pushpak Bhattacharyya:
IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features. 212-218 - Sreekanth Madisetty, Maunendra Sankar Desarkar:
NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets. 219-224 - Antonio Moreno-Ortiz:
Tecnolengua Lingmotif at EmoInt-2017: A lexicon-based approach. 225-232 - Edison Marrese-Taylor, Yutaka Matsuo:
EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity. 233-237 - Yuanye He, Liang-Chih Yu, K. Robert Lai, Weiyi Liu:
YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model. 238-242 - Song Jiang, Xiaotian Han:
DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method. 243-248 - Vineet John, Olga Vechtomova:
UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings. 249-254 - Davide Buscaldi, Belém Priego Sánchez:
LIPN-UAM at EmoInt-2017: Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity Determination. 255-258 - R. Vinayakumar, B. Premjith, S. Sachin Kumar, K. P. Soman, Prabaharan Poornachandran:
deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets. 259-263
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