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Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets
Jeremy Claude Barnes, Roman Klinger, Sabine Schulte im Walde
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Ranking Right-Wing Extremist Social Media Profiles by Similarity to Democratic and Extremist Groups
Matthias Hartung, Roman Klinger, Franziska Schmidtke, Lars Vogel
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IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning
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
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Towards Syntactic Iberian Polarity Classification
David Vilares Calvo, Marcos García González, Miguel Á. Alonso, Carlos Gómez Rodríguez
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Investigating Redundancy in Emoji Use: Study on a Twitter Based Corpus
págs. 118-126
Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach
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
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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
págs. 169-174
NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity
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
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
págs. 200-204
Seernet at EmoInt-2017: Tweet Emotion Intensity Estimator
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IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized
Md Shad Akhtar, Palaash Sawant, Asif Ekbal, Jyoti Pawar, Pushpak Bhattacharyya
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NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets
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Tecnolengua Lingmotif at EmoInt-2017: A lexicon-based approach
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EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity
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YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model
págs. 238-242
DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method
págs. 243-248
UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings
págs. 249-254
LIPN-UAM at EmoInt-2017: Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity Determination
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deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets
R. Vinayakumar, B. Premjith, S. Sachin Kumar, K.P. Soman, Prabaharan Poornachandran
págs. 259-263
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