Second call for papers in the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Event Notification Type: 
Call for Papers
Abbreviated Title: 
WASSA 2017
Co-located with EMNLP 2017
Alexandra Balahur
Saif M. Mohammad
Erik van der Goot
Submission Deadline: 
Saturday, 10 June 2017

Second call for papers in the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA 2017).
And, call for participation in the shared task.


The 8th  Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA 2017) will be held in conjunction with EMNLP-2017. Its aim is to continue the line of the previous editions, bringing together researchers in Computational Linguistics working on Subjectivity and Sentiment Analysis and researchers working on interdisciplinary aspects of affect computation from text. Additionally, starting with WASSA 2013, we extended the focus to Social Media phenomena and the impact of affect-related phenomena in this context.  In this new proposed edition, we would like to encourage the submission of long and short research and demo papers including, but not restricted to the following topics related to subjectivity and sentiment analysis:

• Resources for subjectivity, sentiment and social media analysis; (semi-)automatic corpora generation and annotation
• Opinion retrieval, extraction, categorization, aggregation and summarization
• Trend detection in social media using subjectivity and sentiment analysis techniques
• Data linking through social networks based on affect-related NLP methods
• Impact of affective data from social media
• Mass opinion estimation based on NLP and statistical models
• Online reputation management
• Topic and sentiment studies and applications of topic-sentiment analysis
• Domain, topic and genre dependency of sentiment analysis
• Ambiguity issues and word sense disambiguation of subjective language
• Pragmatic analysis of the opinion mining task
• Use of Semantic Web technologies for subjectivity and sentiment analysis
• Improvement of NLP tasks using subjectivity and/or sentiment analysis
• Intrinsic and extrinsic evaluations subjectivity and sentiment analysis
• Subjectivity, sentiment and emotion detection in social networks
• Classification of stance in dialogues
• Applications of sentiment and social media analysis systems
• Application of theories from other related fields (Neuropsychology, Cognitive Science, Psychology) to subjectivity and sentiment analysis
• Visualizing affect in traditional text sources as well as social media posts

In 2017, we also include a shared task on emotions as part of the workshop. New labeled training and test data will be provided and participants can test their automatic systems on this common dataset. Papers describing the systems will be presented at the WASSA workshop, either as oral presentations (top scoring systems) or as posters.


Task: WASSA-2017 Shared Task on Emotion Intensity (EmoInt)

Given a tweet and an emotion X, determine the intensity or degree of emotion X felt by the speaker -- a real-valued score between 0 and 1. The maximum possible score 1 stands for feeling the maximum amount of emotion X (or having a mental state maximally inclined towards feeling emotion X). The minimum possible score 0 stands for feeling the least amount of emotion X (or having a mental state maximally away from feeling emotion X). The tweet along with the emotion X will be referred to as an instance. Note that the absolute scores have no inherent meaning -- they are used only as a means to convey that the instances with higher scores correspond to a greater degree of emotion X than instances with lower scores.
Data: Training and test datasets will be provided for four emotions: joy, sadness, fear, and anger. For example, the anger training dataset will have tweets along with a real-valued score between 0 and 1 indicating the degree of anger felt by the speaker. More details are on the task webpage.

Task webpage:
Task organizers: Saif M. Mohammad, Felipe Bravo-Marquez, and Alexandra Balahur


Workshop paper submission deadline: June 10, 2017
Author notifications : July 9, 2017
Camera ready submissions due: July 23, 2017


Shared task evaluation period starts: May 02, 2017
Shared task evaluation period ends: May 14, 2017
Shared task results posted: May 21, 2017
Workshop paper submission deadline: June 10, 2017
Author notifications : July 9, 2017
Camera ready submissions due: July 23, 2017


- Alexandra Balahur, European Commission Joint Research Centre, Directorate I, Text and Data Mining Unit,
- Saif M. Mohammad, National Research Council Canada,
- Erik van der Goot, European Commission Joint Research Centre , Directorate I, Text and Data Mining Unit,

Felipe Bravo - University of Waikato, New Zealand
Nicoletta Calzolari - CNR Pisa, Italy
Erik Cambria - University of Stirling, U.K.
Fermin Cruz Mata - University of Seville, Spain
Montse Cuadros - Vicomtech, Spain
Leon Derczynski - University of Sheffield, U.K.
Michael Gamon – Microsoft, U.S.A.
Veronique Hoste - University of Ghent, Belgium
Ruben Izquierdo Bevia – Nuance, Spain
Svetlana Kiritchenko, National Research Council, Canada
Isa Maks - Vrije Universiteit Amsterdam, The Netherlands
Diana Maynard - University of Sheffield, U.K.
Rada Mihalcea - University of Michigan , U.S.A.       
Karo Moilanen - University of Oxford, U.K.
Günter Neumann - DFKI, Germany
Constantin Orasan - University of Wolverhampton, U.K.
Viktor Pekar - University of Wolverhampton, U.K.
Jose-Manuel Perea-Ortega – University of Extremadura, Spain
Maite Martin Valdivia – University of Jaen, Spain
Paolo Rosso - Technical University of Valencia, Spain
Bjoern Schueller – Imperial College London, U.K.
Josef Steinberger - West Bohemia University Prague, The Czech Republic
Maite Taboada – Simon Fraser University, Canada
Mike Thelwall - University of Wolverhampton, U.K
José Antonio Troyano - University of Seville, Spain
Dan Tufis - RACAI, Romania
Alfonso Ureña - University of Jaén, Spain
Marilyn Walker - University of California Santa Cruz, U.S.A.
Janyce Wiebe - University of Pittsburgh, U.S.A.
Michael Wiegand - Saarland University, Germany
Taras Zagibalov - Brantwatch, U.K.


Research in automatic Subjectivity and Sentiment 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 rapid expansion of the Social Web that made it possible for people all over the world to share, comment or consult content on any given topic. In this context, opinions, sentiments and emotions expressed in Social Media texts have been shown to have a high influence on the social and economic behaviour worldwide. SSA systems are highly relevant to many real-world applications (e.g. marketing, eGovernance, business intelligence, social analysis, public health) and also many tasks in NLP – information extraction, question answering, textual entailment, to name just a few.

The importance of this field has been proven by the high number of approaches proposed in research in the past decade, as well as by the interest that it raised from other disciplines (Economics, Sociology, Psychology, Marketing, Crisis Management, Behavioral Studies) and the applications that were created using its technology.

In spite of the growing body of research in the area in the past years, dealing with affective phenomena in text has proven to be a complex, interdisciplinary problem that remains far from being solved. Its challenges include the need to address the issue from different perspectives, at different levels, and different modalities, depending on the characteristics of the textual genre, the language(s) treated and the final application for which the analysis is done. Additionally, SSA from Social Media texts has opened the way to many other types of analyses, linking textual data with images, social network metadata and social-media-specific text markings (e.g. Twitter hashtags).

Finally, the possibility to follow trends on opinions, while comparing and contrasting different sources of information (e.g. mainstream media vs. social media) allows for a more complete view and fairer opinion formation process.

- Alexandra Balahur:
- Saif M. Mohammad:
- Erik van der Goot: