Balahur, Alexandra

Carlo Strapparava, Carlo

De Clercq, Orphée

Hoste, Veronique

Klinger, Roman

English

Ghent University Academic Bibliography

NAACL HLT 2019The Workshop on Computational Approaches toSubjectivity, Sentiment and Social Media AnalysisProceedings of the Tenth WorkshopJune 6, 2019Minneapolis, USAc©2019 The Association for Computational LinguisticsOrder copies of this and other ACL proceedings from:Association for Computational Linguistics (ACL)209 N. Eighth StreetStroudsburg, PA 18360USATel: +1-570-476-8006Fax: +1-570-476-0860acl@aclweb.orgISBN 978-1-950737-12-3iiIntroductionTwo thousand five hundred years ago, Democritus said: “Nothing exists except atoms and empty space.Everything else is opinion.” Today, this saying is omnipresent, as the state of our social interpretationof reality has been denominated by the “post-truth society”. Research in automatic Subjectivity andSentiment Analysis (SSA), as subtasks of Affective Computing and Natural Language Processing (NLP),has flourished in the past years. The growth in interest in these tasks was motivated by the birth and rapidexpansion of the Social Web that made it possible for people all over the world to share, comment orconsult content on any given topic. In this context, opinions, sentiments and emotions expressed in SocialMedia texts have been shown to have a high influence on social and economic behaviour worldwide.SSA systems are highly relevant to many real-world applications (e.g. marketing, eGovernance, businessintelligence, social analysis) and also to many tasks in Natural Language Processing (NLP), e.g.information extraction, question answering, textual entailment, to name just a few. The importance of thisfield has been proven by the high number of approaches proposed in research in the past decade, as wellas by the interest that it raised from other disciplines (Economics, Sociology, Psychology, Marketing,Crisis Management, Digital Humanities and Behavioral Studies) and the applications that were createdusing its technology. Next to the growth in the diversity of applications, task definitions changed towardsmore complex challenges: Subjectivity, polarity recognition and opinion mining has been enriched withfine-grained aspect and target level predictions. Polarity as a concept is complemented by emotionmodels as defined from psychological research. In spite of the growing body of research in the area inthe past years, dealing with affective phenomena in text has proven to be a complex, interdisciplinaryproblem that remains far from being solved. Its challenges include the need to address the issue fromdifferent perspectives and at different levels, depending on the characteristics of the textual genre, thelanguage(s) treated and the final application for which the analysis is done.The aim of the 10th Workshop on Computational Approaches to Subjectivity, Sentiment andSocial Media Analysis (WASSA 2019) was to continue the line of the previous editions, bringingtogether researchers in Computational Linguistics working on Subjectivity and Sentiment Analysis andresearchers working on interdisciplinary aspects of affect computation from text.For this year’s workshop, we accepted 12/19 papers (63 % acceptance rate). The number of submissionshas been comparably low in contrast to previous years, given that WASSA 2018 just took place a littlemore than half a year before WASSA 2019. Nevertheless and given the high quality of submissions, wehave an interesting program consisting of 11 oral presentations in 3 sessions. An invited talk by SvenBuechel on "Modeling Empathy and Distress in Reaction to News Stories" complements this year’s paperpresentations.Accepted papers deal with a variety of topics, like stance detection, topic and aspect level sentimentanalysis, social network analysis, humor detection, and negations and intensifiers in emotion andsentiment analysis, as well as applications of such systems to real-world problems. For the first time, weasked the reviewers for recommendations for a best paper award. Based on these recommendations, theorganizers voted secretly on the best paper selection. The result is that the paper “Stance Detection inCode-Mixed Hindi-English Social Media Data using Multi-Task Learning” by Sushmitha Reddy Sane,Suraj Tripathi, Koushik Reddy Sane and Radhika Mamidi wins this year’s best paper award.We would like to thank the NAACL 2019 Organizers and Workshop Chairs for their help and support atthe different stages of the workshop organization process. We are also especially grateful to the ProgramCommittee members for the time and effort they spent in assessing the papers.Alexandra Balahur, Roman Klinger, Véronique Hoste, Carlo Strapparava and Orphée De ClercqWASSA 2019 ChairsiiiKeynote talk: Modeling Empathy and Distress in Reaction to News StoriesSven BuechelJULIE Lab, Jena University, GermanyAbstractComputational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following major limitations:It underestimates the psychological complexity of the phenomenon, adheres to a weak notion of groundtruth where empathic states are ascribed by third parties, and lacks a shared corpus. In contrast, this talkdescribes the first publicly available gold standard for empathy prediction. It is constructed using a novelannotation methodology which reliably captures empathy assessments by the writer of a statement usingmulti-item scales. This is also the first computational work distinguishing between multiple forms ofempathy, empathic concern, and personal distress, as recognized throughout psychology.ivOrganizersOrganizers:Alexandra Balahur – European Commission Joint Research CentreRoman Klinger – University of Stuttgart, GermanyVéronique Hoste – Ghent University, BelgiumCarlo Strapparava – Fundazione Bruno Kessler, ItalyOrphée de Clercq – Ghent University, BelgiumProgram Committee:Jeremy Barnes – University of OsloSabine Bergler – Concordia UniversityCristina Bosco – Dipartimento di Informatica - Universita di TorinoLaura Ana Maria Bostan – IMS, University of StuttgartSven Buechel – Friedrich-Schiller-Universität JenaErik Cambria – Nanyang Technological UniversityLuna De Bruyne – LT3, Language and Translation Technology Team, Ghent UniversityLingjia Deng – Bloomberg L.P.Antske Fokkens – VU AmsterdamMichael Gamon – Microsoft ResearchLorenzo Gatti – Human Media Interaction, University of TwenteMatthias Hartung – Semalytix GmbHCarlos A. Iglesias – Universidad Politécnica de MadridGilles Jacobs – LT3, Language and Translation Technology Team, Ghent UniversityAditya Joshi – CSIROManfred Klenner – Computational Linguistics, University of ZurichEls Lefever – LT3, Language and Translation Technology Team, Ghent UniversityGerard Lynch – University College DublinIsa Maks – VU University of AmsterdamEdison Marrese-Taylor – The University of TokyoSaif Mohammad – National Research Council CanadaKaro Moilanen – Dpt of Computer Science, University of OxfordMalvina Nissim – University of GroningenSean Papay – University of StuttgartViviana Patti – Dipartimento di Informatica, University of TurinJose Manuel Perea-Ortega – University of ExtremaduraDaniel Preoţiuc-Pietro – BloombergPaolo Rosso – Universitat Politècnica de ValènciaAlon Rozental – Amobee inc.Mohammad Salameh – DiffbotJosef Steinberger – University of West BohemiaMike Thelwall – University of WolverhamptonDan Tufis, – Research Institute for Artificial Intelligence, Romanian AcademyMichael Wiegand – Saarland UniversityMichael Wojatzki – Language Technology Lab, University of Duisburg-EssenTaras Zagibalov – SmallStep LLCvTable of ContentsStance Detection in Code-Mixed Hindi-English Social Media Data using Multi-Task LearningSushmitha Reddy Sane, Suraj Tripathi, Koushik Reddy Sane and Radhika Mamidi . . . . . . . . . . . . . 1A Soft Label Strategy for Target-Level Sentiment ClassificationDa Yin, Xiao Liu, Xiuyu Wu and Baobao Chang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Online abuse detection: the value of preprocessing and neural attention modelsDhruv Kumar, Robin Cohen and Lukasz Golab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion AnalysisLaura Ana Maria Bostan and Roman Klinger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Enhancing the Measurement of Social Effects by Capturing MoralityRezvaneh Rezapour, Saumil H. Shah and Jana Diesner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Using Structured Representation and Data: A Hybrid Model for Negation and Sentiment in CustomerService ConversationsAmita Misra, Mansurul Bhuiyan, Jalal Mahmud and Saurabh Tripathy . . . . . . . . . . . . . . . . . . . . . . . 46Deep Learning Techniques for Humor Detection in Hindi-English Code-Mixed TweetsSushmitha Reddy Sane, Suraj Tripathi, Koushik Reddy Sane and Radhika Mamidi . . . . . . . . . . . . 57How do we feel when a robot dies? Emotions expressed on Twitter before and after hitchBOT’s destruc-tionKathleen C. Fraser, Frauke Zeller, David Harris Smith, Saif Mohammad and Frank Rudzicz . . . . 62"When Numbers Matter!": Detecting Sarcasm in Numerical Portions of TextAbhijeet Dubey, Lakshya Kumar, Arpan Somani, Aditya Joshi and Pushpak Bhattacharyya . . . . 72Cross-lingual Subjectivity Detection for Resource Lean LanguagesIda Amini, Samane Karimi and Azadeh Shakery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81Analyzing Incorporation of Emotion in Emoji PredictionShirley Anugrah Hayati and Aldrian Obaja Muis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91viiConference ProgramThursday, June 6, 20199:00–9:15 Opening of the WASSA Workshop by Alexandra Balahur9:15–10:15 Invited Talk by Sven Buechel: Modeling Empathy and Distress in Reaction toNews Stories10:15–10:45 Morning Coffee BreakOral Session 110:45–11:10 Stance Detection in Code-Mixed Hindi-English Social Media Data using Multi-TaskLearningSushmitha Reddy Sane, Suraj Tripathi, Koushik Reddy Sane and Radhika Mamidi11:10–11:35 A Soft Label Strategy for Target-Level Sentiment ClassificationDa Yin, Xiao Liu, Xiuyu Wu and Baobao Chang11:35–12:00 Online abuse detection: the value of preprocessing and neural attention modelsDhruv Kumar, Robin Cohen and Lukasz Golab12:00–12:25 Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion AnalysisLaura Ana Maria Bostan and Roman Klinger12:25–14:00 Lunch BreakixThursday, June 6, 2019 (continued)Oral Session 214:00–14:25 Enhancing the Measurement of Social Effects by Capturing MoralityRezvaneh Rezapour, Saumil H. Shah and Jana Diesner14:25–14:50 Using Structured Representation and Data: A Hybrid Model for Negation and Sen-timent in Customer Service ConversationsAmita Misra, Mansurul Bhuiyan, Jalal Mahmud and Saurabh Tripathy14:50–15:15 Deep Learning Techniques for Humor Detection in Hindi-English Code-MixedTweetsSushmitha Reddy Sane, Suraj Tripathi, Koushik Reddy Sane and Radhika Mamidi15:15–15:40 How do we feel when a robot dies? Emotions expressed on Twitter before and afterhitchBOT’s destructionKathleen C. Fraser, Frauke Zeller, David Harris Smith, Saif Mohammad and FrankRudzicz15:40–16:10 Afternoon Coffee BreakOral Session 316:10–16:35 "When Numbers Matter!": Detecting Sarcasm in Numerical Portions of TextAbhijeet Dubey, Lakshya Kumar, Arpan Somani, Aditya Joshi and Pushpak Bhat-tacharyya16:35–17:00 Cross-lingual Subjectivity Detection for Resource Lean LanguagesIda Amini, Samane Karimi and Azadeh Shakery17:00–17:25 Analyzing Incorporation of Emotion in Emoji PredictionShirley Anugrah Hayati and Aldrian Obaja Muis17:25–18:00 Closing Remarksx

Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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