[Elsnet-list] WASSA-2017 CFP: 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 Feb 13 20:10:19 CET 2017

Call for papers and participation in the 8th Workshop on Computational
Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA 2017)


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
• Trend detection in social media using subjectivity and sentiment analysis
• 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

In 2017, we also include two shared tasks 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 1: Emotion intensity recognition from tweets

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

Task 2: Emotion Linking and Classification (EmoLinC)

Given a tweet about a topic/target, link it to a human need, motivation,
objective, desire, goal and classify it according to either the
emotion/emotions the author is most likely intending to convey, the lack of
emotion or the fact that the text is sarcastic/ironic. . More details on
the WASSA 2017 website.


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-
- 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

- 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
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