[Corpora-List] WASSA-2017 CFP: Second call for papers and participation in the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Saif Mohammad uvgotsaif at gmail.com
Mon May 8 19:30:20 CEST 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: http://saifmohammad.com/WebPages/EmotionIntensity-SharedTask.html

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, alexandra.balahur at jrc.ec.europa.eu

- Saif M. Mohammad, National Research Council Canada, saif.mohammad at nrc-cnrc.gc.ca

- Erik van der Goot, European Commission Joint Research Centre , Directorate I, Text and Data Mining Unit, Erik.van-der-Goot at jrc.ec.europa.eu


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: alexandra.balahur at jrc.ec.europa.eu

- Saif M. Mohammad: saif.mohammad at nrc-cnrc.gc.ca

- Erik van der Goot: Erik.van-der-Goot at jrc.ec.europa.eu

-- Saif M. Mohammad Senior Research Officer National Research Council Canada http://www.saifmohammad.com -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: text/html Size: 42915 bytes Desc: not available URL: <https://mailman.uib.no/public/corpora/attachments/20170508/441006e6/attachment.txt>

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