Abstract is missing.
- Detecting Sarcasm Using Different Forms Of IncongruityAditya Joshi. 1 [doi]
- Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment DatasetsJeremy Barnes, Roman Klinger, Sabine Schulte im Walde. 2-12 [doi]
- Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection CorpusHendrik Schuff, Jeremy Barnes, Julian Mohme, Sebastian Padó, Roman Klinger. 13-23 [doi]
- Ranking Right-Wing Extremist Social Media Profiles by Similarity to Democratic and Extremist GroupsMatthias Hartung, Roman Klinger, Franziska Schmidtke, Lars Vogel. 24-33 [doi]
- WASSA-2017 Shared Task on Emotion IntensitySaif Mohammad, Felipe Bravo-Marquez. 34-49 [doi]
- IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep LearningMaximilian Köper, Evgeny Kim, Roman Klinger. 50-57 [doi]
- Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in TweetsPrayas Jain, Pranav Goel, Devang Kulshreshtha, Kaushal Kumar Shukla. 58-65 [doi]
- Latest News in Computational Argumentation: Surfing on the Deep Learning Wave, Scuba Diving in the Abyss of Fundamental QuestionsIryna Gurevych. 66 [doi]
- Towards Syntactic Iberian Polarity ClassificationDavid Vilares, Marcos García, Miguel A. Alonso, Carlos Gómez-Rodríguez. 67-73 [doi]
- Toward Stance Classification Based on Claim MicrostructuresFilip Boltuzic, Jan Snajder. 74-80 [doi]
- Linguistic Reflexes of Well-Being and Happiness in EchoJiaQi Wu, Marilyn A. Walker, Pranav Anand, Steve Whittaker. 81-91 [doi]
- Forecasting Consumer Spending from Purchase Intentions Expressed on Social MediaViktor Pekar, Jane Binner. 92-101 [doi]
- Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNNEdison Marrese-Taylor, Jorge A. Balazs, Yutaka Matsuo. 102-111 [doi]
- Understanding human values and their emotional effectAlexandra Balahur. 112 [doi]
- Did you ever read about Frogs drinking Coffee? Investigating the Compositionality of Multi-Emoji ExpressionsRebeca Padilla López, Fabienne Cap. 113-117 [doi]
- Investigating Redundancy in Emoji Use: Study on a Twitter Based CorpusGiulia Donato, Patrizia Paggio. 118-126 [doi]
- Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net ApproachKishaloy Halder, Lahari Poddar, Min-Yen Kan. 127-135 [doi]
- Towards an integrated pipeline for aspect-based sentiment analysis in various domainsOrphée De Clercq, Els Lefever, Gilles Jacobs, Tijl Carpels, Véronique Hoste. 136-142 [doi]
- Building a SentiWordNet for OdiaGaurav Mohanty, Abishek Kannan, Radhika Mamidi. 143-148 [doi]
- Lexicon Integrated CNN Models with Attention for Sentiment AnalysisBonggun Shin, Timothy Lee, Jinho D. Choi. 149-158 [doi]
- Explaining Recurrent Neural Network Predictions in Sentiment AnalysisLeila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek. 159-168 [doi]
- GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity DetectionEgor Lakomkin, Chandrakant Bothe, Stefan Wermter. 169-174 [doi]
- NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion IntensityVladimir Andryushechkin, Ian Wood, James O'Neill. 175-179 [doi]
- Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled DatasetsAthanasios Giannakopoulos, Claudiu Musat, Andreea Hossmann, Michael Baeriswyl. 180-188 [doi]
- PLN-PUCRS at EmoInt-2017: Psycholinguistic features for emotion intensity prediction in tweetsHenrique D. P. dos Santos, Renata Vieira. 189-192 [doi]
- Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English TweetsHardik Meisheri, Rupsa Saha, Priyanka Sinha, Lipika Dey. 193-199 [doi]
- YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity PredictionYou Zhang, Hang Yuan, Jin Wang, Xuejie Zhang. 200-204 [doi]
- Seernet at EmoInt-2017: Tweet Emotion Intensity EstimatorVenkatesh Duppada, Sushant Hiray. 205-211 [doi]
- IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized FeaturesMd. Shad Akhtar, Palaash Sawant, Asif Ekbal, Jyoti Pawar, Pushpak Bhattacharyya. 212-218 [doi]
- NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in TweetsSreekanth Madisetty, Maunendra Sankar Desarkar. 219-224 [doi]
- Tecnolengua Lingmotif at EmoInt-2017: A lexicon-based approachAntonio Moreno Ortiz. 225-232 [doi]
- EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion IntensityEdison Marrese-Taylor, Yutaka Matsuo. 233-237 [doi]
- YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN ModelYuanye He, Liang-Chih Yu, K. Robert Lai, Weiyi Liu. 238-242 [doi]
- DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble MethodSong Jiang, Xiaotian Han. 243-248 [doi]
- UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word EmbeddingsVineet John, Olga Vechtomova. 249-254 [doi]
- LIPN-UAM at EmoInt-2017: Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity DeterminationDavide Buscaldi, Belém Priego Sanchez. 255-258 [doi]
- deepCybErNet at EmoInt-2017: Deep Emotion Intensities in TweetsR. Vinayakumar, B. Premjith, S. Sachin Kumar, K. P. Soman, Prabaharan Poornachandran. 259-263 [doi]