12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA 2022)

ACL’22, Dublin, Ireland, 26 May 2022

Background and Envisaged Scope

Starting with reviews on products on e-commerce sites and ending with the emotional effect presentin or intended by media coverage, research in automatic Subjectivity and Sentiment Analysis as wellas explicit and implicit Emotion Detection and Classification has flourished in the past years. Theimportance of the field has been proven by the high number of approaches proposed in research in thepast decade, as well as by the interest it generated in other disciplines, such as Economics, Sociology,Psychology, Marketing, Crisis Management Digital Humanities. Building on previous editions, the aim of WASSA 2022 is to bring together researchers workingon Subjectivity, Sentiment Analysis, Emotion Detection and Classification and their applications toother NLP or real-world tasks (e.g. public health messaging, fake news, media impact analysis, socialmedia mining, computational literary studies) and researchers working on interdisciplinary aspectsof affect computation from text. For this edition, we encourage the submission of long and shortresearch and demo papers including, but not restricted to the following topics:

  • 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
  • Humor, Irony and Sarcasm detection
  • Mis and disinformation analysis and the role of affective attributes
  • Aspect and topic-based sentiment and emotion analysis
  • Analysis of stable traits of social media users, incl. personality analysis and profiling
  • 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
  • The role of emotions in argument mining
  • Application of theories from related fields to subjectivity and sentiment analysis
  • Multimodal emotion detection and classification
  • Applications of sentiment and emotion mining

Important dates

  • Feb. 28 March 5, 2022– Submission deadline.
  • March 15, 2022 – Commitment deadline for submitting through ARR with reviews
  • March 26, 2022 – Notification of acceptance.
  • April 10, 2022 – Camera-ready papers due.
  • May 26, 2022 – Workshop.

All deadlines are 23:59 UTC-12.


At WASSA 2022, we will accept three types of submissions: For the regular research track we accept long & short papers.

Additionally, we accept double submissions and double commitment of ARR reviews in parallel to WASSA and another venue. Please note that you must immediately withdraw your paper from WASSA if you decide to publish it elsewhere.

Long papers

Long papers may consist of up to eight (8) pages of content, with any number of additional pages of references, and will be presented orally.

Short papers

Short papers may consist of up to four (4) pages of content, with two (2) additional pages of references, and will be presented either orally or as a poster.

Demo papers

New this year is that we also introduce an industry track, for which we accept demo papers:

  • Demo papers describe system demonstrations, ranging from early prototypes to mature production-ready systems. Please note that Please note: Commercial sales and marketing activities are not appropriate for this track. Demo papers may consist of up to six (6) pages of content, these will be presented as a poster and should include a live demonstration. For more information click here.

Additionally, system description papers from the shared task will be presented either orally or as poster.

Submission procedure and templates

Submissions without reviews can be done directly through our OpenReview website.

Authors who received reviews already through the ACL Rolling Review process are invited to commit their reviewed paper to WASSA. To do so, please go to https://openreview.net/group?id=aclweb.org/ACL/2022/Workshop/WASSA on OpenReview and click on “ACL 2022 Workshop WASSA Commitment Submission”. You will then need to add the title, the URL to the ARR submission with reviews + metareview, and other information. The commitment date for ARR papers with reviews is March 15.

Both long and short papers must be anonymised for double-blind reviewing, must follow the ACL Author Guidelines, and must use the ACL 2022 templates available on the ACL Rolling Review website. The submitting author must have an OpenReview profile. Please ensure profiles are complete before submission. This tutorial from the ACL Rolling Review might be helpful.

Optional Supplementary Materials: Appendices, Software and Data

ARR encourages the submission of these supplementary materials to improve the reproducibility of results, and to enable authors to provide additional information that does not fit in the paper. Supplementary materials may include appendices, software or data. For example, pre processing decisions, model parameters, feature templates, lengthy proofs or derivations, pseudocode, sample system inputs/outputs, and other details that are necessary for the exact replication of the work described in the paper can be put into appendices. However, if the pseudo-code or derivations or model specifications are an important part of the contribution, or if they are important for the reviewers to assess the technical correctness of the work, they should be a part of the main paper, and not appear in appendices. Reviewers are not required to consider material in appendices. Appendices should come after the references in the submitted pdf, but do not count towards the page limit. Software should be submitted as a single .tgz or .zip archive, and data as a separate single .tgz or .zip archive. Supplementary materials must be fully anonymized to preserve the two-way anonymized reviewing policy.