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