EACL 2021 WASSA 2021
11th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
To be held in conjuntion with the EACL 2021  Conference

European Commission Joint Research Centre

University of Ghent

27.04.2021Recording of invited talk by Lyle Ungar available online.
19.04.2021WASSA 2021 was great! Thank you all for your participation and hope to see you again soon!
01.04.2021program available online.
02.02.2021CodaLab platform for shared task online.
18.01.2021Second Call for Papers and website updated.
27.12.2020The WASSA 2021 shared task has been launched.
27.11.2020The Call for Papers has been sent out. You can find a pdf version here.
16.11.2020The WASSA 2021 website is online.
16.11.2020The WASSA 2021 Shared Task will be announced shortly.
Starting with reviews on products on e-commerce sites and ending with the emotional effect present in or intended by media coverage, research in automatic Subjectivity and Sentiment Analysis as well as explicit and implicit Emotion Detection and Classification has flourished in the past years. The importance of the field has been proven by the high number of approaches proposed in research in the past decade, as well as by the interest it generated in other disciplines, such as Economics, Sociology, Psychology, Marketing, Crisis Management & Digital Humanities. In Medicine, insights from the field can provide actionable insights into the efficacy of public health messaging. Since the beginning of 2020, COVID-19 has dominated the news headlines all around the world and evoked a variety of emotions amongst the general public. Understanding these emotions not only gives insights into the way the public responds to the COVID-19 pandemic in itself and to the media coverage of the disease, but might help to encourage health promotion measures.

Building on previous editions, the aim of WASSA 2021 is to bring together researchers working on Subjectivity, Sentiment Analysis, Emotion Detection and Classification and their applications to other NLP or real world tasks (e.g. public health messaging, fake news, media impact analysis) and researchers working on interdisciplinary aspects of affect computation from text. We strongly encourage submissions that tackle sentiment or emotion detection and classification in the context of the COVID-19 pandemic. For this edition, we encourage the submission of long and short research and demo papers including, but not restricted to the following topics:

A downloadable version of the CfP will be available soon. WASSA 2021 anti-harrassment policy following the ACL anti-harassment policy. https://www.aclweb.org/adminwiki/index.php?title=Anti-Harassment_Policy


We encourage the submission of long and short papers including novel research contributions, system demonstration papers, negative results, and opinion pieces including, not restricted to the following topics, however, all related to subjectivity, sentiment, emotion, opinion mining and social media analysis:

  • Public sentiments and communication patterns of public health emergencies, e.g. COVID-19
  • Resources for subjectivity, sentiment, emotion and social media analysis
  • Opinion retrieval, extraction, categorization, aggregation and summarization
  • Trend detection in social media using subjectivity, sentiment and emotion analysis
  • Humor, Irony and Sarcasm detection
  • The role of emotion and affective phenomena in dis/misinformation
  • Online reputation management
  • Aspect and topic-based sentiment analysis
  • Transfer learning for domain, language and genre portability of sentiment analysis
  • Modelling commonsense knowledge for subjectivity, sentiment or emotion analysis
  • Improvement of NLP tasks using subjectivity and/or sentiment analysis
  • Intrinsic and extrinsic evaluation of subjectivity and/or sentiment analysis
  • Detecting and quantifying the emotional effect of factual arguments
  • Application of theories from other related fields to subjectivity and sentiment analysis
  • Implicit sentiment and bias analysis in newswire text
  • Multimodal emotion detection and classification
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