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DEADLINE EXTENSION - Workshop on Emotions, Modality, Sentiment Analysis and the Semantic Web @ ESWC2016.

From: Mauro Dragoni <dragoni@fbk.eu>
Date: Mon, 7 Mar 2016 15:31:24 +0100
Message-ID: <CAFvSjQsYw65GH5d7SHmNym5744=tjqoxVn_QQGonNUvev12bgQ@mail.gmail.com>
To: undisclosed-recipients:;
Call For Paper: Workshop on Emotions, Modality, Sentiment Analysis and the
Semantic Web @ ESWC2016.
Dates: May 29th 2016
Venue: Crete, Greece
Hashtag: #SentimentAnalysis
Conference Site: http://2016.eswc-conferences.org
Workshop Site: http://www.maurodragoni.com/research/opinionmining/events/
Easychair Submission Page:

As the Web rapidly evolves, people are becoming increasingly enthusiastic
about interacting, sharing, and collaborating through social networks,
online communities, blogs, wikis, and the like. In recent years, this
collective intelligence has spread to many different areas, with particular
focus on fields related to everyday life such as commerce, tourism,
education, and health, causing the size of the social Web to expand

To identify the emotions (e.g. sentiment polarity, sadness, happiness,
anger, irony, sarcasm, etc.) and the modality (e.g. doubt, certainty,
obligation, liability, desire, etc.) expressed in this continuously growing
content is critical to enable the correct interpretation of the opinions
expressed or reported about social events, political movements, company
strategies, marketing campaigns, product preferences, etc.

This has raised growing interest both within the scientific community, by
providing it with new research challenges, as well as in the business
world, as applications such as marketing and financial prediction would
gain remarkable benefits.

One of the main application tasks in this context is opinion mining [1],
which is addressed by a significant number of Natural Language Processing
techniques, e.g. for distinguishing objective from subjective statements
[2], as well as for more fine-grained analysis of sentiment, such as
polarity and emotions [8]. Recently, this has been extended to the
detection of irony, humour, and other forms of figurative language [3]. In
practice, this has led to the organisation of a series of shared tasks on
sentiment analysis, including irony and figurative language detection
(SemEval 2013, 2014, 2015), with the production of annotated data and
development of running systems.

However, existing solutions still have many limitations leaving the
challenge of emotions and modality analysis still open. For example, there
is the need for building/enriching semantic/cognitive resources for
supporting emotion and modality recognition and analysis. Additionally, the
joint treatment of modality and emotion is, computationally, trailing
behind, and therefore the focus of ongoing, current research. Also, while
we can produce rather robust deep semantic analysis of natural language, we
still need to tune this analysis towards the processing of sentiment and
modalities, which cannot be addressed by means of statistical models only,
currently the prevailing approaches to sentiment analysis in NLP. The
hybridization of NLP techniques with Semantic Web technologies is therefore
a direction worth exploring, as recently shown in.

Based on the lessons learnt from the first edition, this year the scope of
the workshop is a bit broader (although still focusing on a very specific
domain) and accepted submissions will include abstracts and position papers
in addition to full papers. The workshops main focus will be discussion
rather than presentations, which are seen as seeds for boosting discussion
topics, and an expected result will be a joint manifesto and a research
roadmap that will provide the Semantic Web community with inspiring
research challenges.

The Workshop will be connected to the ESWC 2016 Fine-Grained Sentiment
Analysis Challenge (https://github.com/diegoref/SSA2016). Both the Workshop
and the Challenge can benefit from a Google Group, called Semantic
Sentiment Analysis Initiative (

*** Topics of interest ***

Includes but not limited to:
* Ontologies and knowledge bases for emotion recognition
* Topic and entity based emotion recognition
* Semantics in the evolution of emotions within and across social media
systems and topics
* Semantic processing of social media for emotion recognition
* Contextualised emotion recognition
* Comparison of semantic approaches for emotion recognition
* Personalised semantic emotion recognition and monitoring
* Using semantics for prediction of emotions towards events, people,
organisations, etc.
* Baselines and datasets for semantic emotion recognition
* Semantics in stream-based emotion recognition
* Comparison between semantic and non-semantic approaches for emotion
* Multimodal emotion recognition
* Multilingual sentiment analysis
* Challenges in using semantics for emotion recognition

*** Submissions ***

Submission criteria are the following:
* Papers must comply with the LNCS style
* Full research papers (up to 8-10 pages)
* Short research papers (up to 4-6 pages)
* Position papers (2 pages)
Papers are submitted in PDF format via the workshop’s EasyChair submission
pages: https://easychair.org/conferences/?conf=emsasw2016
Accepted papers will be published by CEUR–WS. The best paper (according to
the reviewers’ rate) will be published within the main conference
proceedings. We already sent a form request to Springer to include Workshop
papers in a Springer book. If the answer is positive (we should know this
by mid March 2016) then the accepted papers will be published within the
Springer book and not CEUR-WS.

At least one of the authors of the accepted papers must register for both
the main conference and the workshop to be included into the workshop

*** Important dates ***

March 18th, 2016, 23:59 CET: Full, Short, and Position papers submission
April 1st, 2016, 23:59 CET: Notification of acceptance
April 15th, 2016, 23:59 CET: Camera-ready paper due
ESWC 2016 Workshop day: Sunday, May 29th

*** Workshop Chairs ***
Prof. Diego Reforgiato Recupero
Dr. Mauro Dragoni

*** References ***
[1]  Bo, P., and Lee, L. (2008). Opinion mining and sentiment analysis.
Foundations and Trends in Information Retrieval , 2 (1-2), 1-135.
[2]  Wiebe, J., and Ellen, R. (2005). Creating Subjective and Objective
Sentence Classifiers from Unannotated Texts. Computational Linguistics and
Intelligent Text Processing 6th International Conference, CICLing (pp.
486-497). Mexico City: Springer.
[3]  Paula, C., Sarmento, L., Silva, M. J., and de Oliveira, E. (2009).
Clues for detecting irony in user-generated contents: oh…!! it’s so
easy;-). Proceedings of the 1st international CIKM workshop on
Topic-sentiment analysis for mass opinion (pp. 53-56). ACM.
[4]  Reforgiato Recupero, D., Presutti, V., Consoli, S., and Gangemi, A.
(2014). Sentilo: Frame-Based Sentiment Analysis. Cognitive Computation ,
[5]  Saif, H., He, Y., and Alani, H. (2012). Semantic sentiment analysis of
Twitter. 11th International Semantic Web Conference (ISWC 2012) (pp.
508-524). Springer.
[6]  Gangemi, A., Presutti, V., and Reforgiato Recupero, D. (2014). Frame-
based detection of opinion holders and topics: a model and a tool. IEEE
Computational Intelligence , 9 (1), 20-30.
[7]  Cambria, E., and Hussain, A. (2012). Sentic Computing: Techniques,
Tools, and Applications. Springer.
[8]  Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis
Lectures on Human Language Technologies. Chicago: Morgan & Claypool

Dr. Mauro Dragoni
Post-Doc Researcher at Fondazione Bruno Kessler (FBK-IRST)
Via Sommarive 18, 38123, Povo, Trento, Italy
Tel. 0461-314053
Received on Monday, 7 March 2016 14:32:42 UTC

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