SEMOD2015

2015-01-17T15:01:57Z

Diegoreforgiato: /* Workshop format */

SEMOD2015

2015-01-17T15:01:41Z

Diegoreforgiato: /* Motivation and relevance for the Semantic Web community */

SEMOD2015

2015-01-17T15:01:27Z

Diegoreforgiato: /* Topics of Interest */

2014-05-20T08:00:43Z

Diegoreforgiato:

SemanticSentimentAnalysis2014

2014-05-20T08:00:28Z

Diegoreforgiato:

[[Category:Event]]

Semantic Sentiment Analysis 2014

The First Workshop on Semantic Sentiment Analysis at [http://2014.eswc-conferences.org/ ESWC2014], Hersonissos, Crete, 25 May, 2014

==Semantic Web and Sentiment Analysis==

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 exponentially.

The opportunity to capture the sentiment of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing prediction. However, the distillation of knowledge from such a large amount of unstructured information, is so difficult that hybridizing different methods from complementary disciplines facing similar challenges is a key activity.

Various Natural Language Processing techniques have been applied to process texts to detect subjective statements and their sentiment. This task is known as sentiment analysis, and overlaps with opinion mining.
Sentiment analysis over social media faces several challenges due to informal language, uncommon abbreviations, condensed text, ambiguity, illusive context, etc. Much work in recent years focused on investigating new methods for overcoming these problems to increase sentiment analysis accuracy over Twitter and the like.

Semantics can play an important role in enhancing our ability to accurately monitor sentiment over social media with respect to specific concept and topics. For example, using semantics will enable us to extract and distinguish sentiment about, say Berlusconi, in politics, business, criminal investigations, football, or for different events that involves him.
When moving from one context to another, or from one event to another, opinions can shift from positive to negative, or neutral. Semantics can capture this evolution and to differentiate its results accordingly, whereas most existing sentiment analysis systems provide an analysis that can be too coarse-grained, due to missed contextualization.

Mining opinions and sentiments from natural language is a difficult task as opinions and sentiments are often conveyed implicitly through latent semantics, which make approaches based on surface lexical hints less effective in those cases. To this end, semantic sentiment analysis may go beyond word-level analysis of text, and provide novel approaches to opinion mining and sentiment analysis that allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.

Semantic sentiment analysis may benefit from the use of web ontologies (e.g. linked data, multilingual linked lexica, linked vocabularies), or graph-based quasi-ontologies (e.g. ConceptNet, SenticNet, Nell, OIE, etc.) which enable the aggregation of conceptual and affective information associated with natural language opinions.

This quick overview already gives room to the role of a founded, semantic sentiment analysis, capable of distinguishing the different tasks and roles involved in features, methods, resources, and tools for sentiment analysis, and opening to the rich background knowledge that exists, or can be generated and linked within the semantic web.

Therefore, this workshop focuses on the introduction, presentation, and discussion of novel approaches to semantic sentiment analysis. Special focus will be given to semantic methods, models, and tools that exploit common-sense knowledge bases to perform multi- or open-domain sentiment analysis.

The intended audience of the workshop includes researchers from academia and industry as well as professionals and industrial practitioners to discuss and exchange positions on new hybrid techniques, which use semantics for sentiment analysis. The expected number of participants ranges between 20 and 40.

== Topics of Interest ==
Includes but not limited to:
* Ontologies and knowledge bases for sentiment analysis
* Topic and entity based sentiment analysis
* Evolution of sentiment within and across social media systems and topics
* Entity-based sentiment analysis
* Semantic processing of social media for sentiment analysis
* Contextualised sentiment analysis
* Comparison of semantic approaches for sentiment analysis
* Personalised sentiment analysis and monitoring
* Prediction of sentiment towards events, people, organisations, etc.
* Baselines and datasets for semantic sentiment analysis

==Workshop format==

This half-day workshop will consist in an invited lecture by a prominent figure in a related field (e.g. sentic computing, SA-related linked data, etc.), followed by presentations of either full or position papers, demos, and general discussion. A session will be dedicated to a summary of the Concept-based Sentiment Analysis Challenge that is separately organized in the same ESWC2014 Conference.

==Submissions==

Submissions must comply with the Springer LNCS style and will be made using [https://www.easychair.org/conferences/?conf=ssa14 EasyChair].

Authors are invited to submit:

* Full papers (up to 8 pages)
* Short and position papers (up to 4 pages)

Accepted papers will be published by 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 proceedings.

==Important dates==

* <strike>Submission deadline: March 6, 2014 </strike>
* <strike>Submission deadline (Extended): March 10, 2014</strike>
* Submission deadline (Extended but abstract needed ASAP): March 14, 2014
* Notifications: April 1, 2014
* Camera ready version: April 15, 2014

==Workshop chairs:==

* Aldo Gangemi (contact person), U. Paris Nord France/ISTC-CNR Rome Italy, aldo.gangemi@lipn.univ-paris13.fr, http://istc.cnr.it/people/aldo-gangemi
* Harith Alani, KMI-OU Milton Keynes UK, h.alani@open.ac.uk, http://people.kmi.open.ac.uk/harith/
* Malvina Nissim, University of Bologna, malvina.nissim@unibo.it http://corpora.ficlit.unibo.it/People/Nissim/index.html
* Erik Cambria, NUS Singapore, cambria@nus.edu.sg, http://sentic.net
* Diego Reforgiato, ISTC-CNR Catania Italy, diego.reforgiato@istc.cnr.it, http://www.istc.cnr.it/people/diego-reforgiato-recupero

==Program Committee:==

* Valerio Basile, Rijksuniversiteit Groningen, The Netherlands
* Paul Buitelaar, National University of Ireland, Galway, Ireland
* Davide Buscaldi, University Paris Nord, France
* Catherine Havasi, MIT Boston, USA
* Chenghua Lin, University of Aberdeen, UK
* Paolo Rosso, Universitat Politècnica de València, Spain
* Hassan Saif, KMi, OU
* J. Fernando Sánchez-Rada, Universidad Politécnica de Madrid, Spain
* Verónica Perez Rosas, University of North-Texas, USA
* Bebo White, University of Stanford, USA

==Relevant References==

# Sentiment 140. http://www.sentiment140.com/.
# Opinion Crawl. http://opinioncrawl.com/.
# Social Mention. http://www.socialmention.com/.
# Chenghua Lin, Yulan He, Richard Everson, and Stefan Ruger. Weakly supervised joint sentiment-topic detection from text. IEEE Transactions on Knowledge and Data Engineering, 24(6):1134–1145, 2012.
# Keke Cai, Scott Spangler, Ying Chen, and Li Zhang. Leveraging sentiment analysis for topic detection. Web Intelli. and Agent Sys., 8(3):291–302, August 2010.
# Ivan Titov and Ryan McDonald. Modeling online reviews with multi- grain topic models. In Proceedings of the 17th international conference on World Wide Web, WWW ’08, pages 111–120, New York, NY, USA, 2008. ACM.
# Richard Johansson and Alessandro Moschitti. Relational features in fine-grained opinion analysis. Computational Linguistics, 39(3):473–509, 2013.
# Aldo Gangemi, Valentina Presutti, Diego Reforgiato Recupero. Frame-based detection of opinion holders and topics: a model and a tool. IEEE Computational Intelligence, 9(1), 2014.
# E. Cambria and A. Hussain. Sentic computing: Techniques, tools, and applications. Dordrecht, Netherlands: Springer, ISBN: 978-94-007- 5069-2, 2012.
# Bing Liu (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers.
# Saif, Hassan; He, Yulan and Alani, Harith (2012). Semantic sentiment analysis of Twitter. In: The 11th International Semantic Web Conference (ISWC 2012), 11-15 November 2012, Boston, MA, USA.

==Workshop Schedule==
Check [http://stlab.istc.cnr.it/software/SSA14-ESWC/ScheduleWorkshop.png here]
[[Media:http://stlab.istc.cnr.it/software/SSA14-ESWC/ScheduleWorkshop.png]]
[Media:http://stlab.istc.cnr.it/software/SSA14-ESWC/ScheduleWorkshop.png]
[[Image:http://stlab.istc.cnr.it/software/SSA14-ESWC/ScheduleWorkshop.png]]
[Image:http://stlab.istc.cnr.it/software/SSA14-ESWC/ScheduleWorkshop.png]
{{Event
|Name=Semantic Sentiment Analysis 2014
|HasStartDate=2014/05/25
|HasEndDate=2014/05/25
}}
{{Submission deadline
|deadline=2014/03/06
|expired=no
|intime=6 March, 2014
}}

SemanticSentimentAnalysis2014

2014-05-20T08:00:00Z

Diegoreforgiato:

[[Category:Event]]

Semantic Sentiment Analysis 2014

The First Workshop on Semantic Sentiment Analysis at [http://2014.eswc-conferences.org/ ESWC2014], Hersonissos, Crete, 25 May, 2014

==Semantic Web and Sentiment Analysis==

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 exponentially.

The opportunity to capture the sentiment of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing prediction. However, the distillation of knowledge from such a large amount of unstructured information, is so difficult that hybridizing different methods from complementary disciplines facing similar challenges is a key activity.

Various Natural Language Processing techniques have been applied to process texts to detect subjective statements and their sentiment. This task is known as sentiment analysis, and overlaps with opinion mining.
Sentiment analysis over social media faces several challenges due to informal language, uncommon abbreviations, condensed text, ambiguity, illusive context, etc. Much work in recent years focused on investigating new methods for overcoming these problems to increase sentiment analysis accuracy over Twitter and the like.

Semantics can play an important role in enhancing our ability to accurately monitor sentiment over social media with respect to specific concept and topics. For example, using semantics will enable us to extract and distinguish sentiment about, say Berlusconi, in politics, business, criminal investigations, football, or for different events that involves him.
When moving from one context to another, or from one event to another, opinions can shift from positive to negative, or neutral. Semantics can capture this evolution and to differentiate its results accordingly, whereas most existing sentiment analysis systems provide an analysis that can be too coarse-grained, due to missed contextualization.

Mining opinions and sentiments from natural language is a difficult task as opinions and sentiments are often conveyed implicitly through latent semantics, which make approaches based on surface lexical hints less effective in those cases. To this end, semantic sentiment analysis may go beyond word-level analysis of text, and provide novel approaches to opinion mining and sentiment analysis that allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.

Semantic sentiment analysis may benefit from the use of web ontologies (e.g. linked data, multilingual linked lexica, linked vocabularies), or graph-based quasi-ontologies (e.g. ConceptNet, SenticNet, Nell, OIE, etc.) which enable the aggregation of conceptual and affective information associated with natural language opinions.

This quick overview already gives room to the role of a founded, semantic sentiment analysis, capable of distinguishing the different tasks and roles involved in features, methods, resources, and tools for sentiment analysis, and opening to the rich background knowledge that exists, or can be generated and linked within the semantic web.

Therefore, this workshop focuses on the introduction, presentation, and discussion of novel approaches to semantic sentiment analysis. Special focus will be given to semantic methods, models, and tools that exploit common-sense knowledge bases to perform multi- or open-domain sentiment analysis.

The intended audience of the workshop includes researchers from academia and industry as well as professionals and industrial practitioners to discuss and exchange positions on new hybrid techniques, which use semantics for sentiment analysis. The expected number of participants ranges between 20 and 40.

== Topics of Interest ==
Includes but not limited to:
* Ontologies and knowledge bases for sentiment analysis
* Topic and entity based sentiment analysis
* Evolution of sentiment within and across social media systems and topics
* Entity-based sentiment analysis
* Semantic processing of social media for sentiment analysis
* Contextualised sentiment analysis
* Comparison of semantic approaches for sentiment analysis
* Personalised sentiment analysis and monitoring
* Prediction of sentiment towards events, people, organisations, etc.
* Baselines and datasets for semantic sentiment analysis

==Workshop format==

This half-day workshop will consist in an invited lecture by a prominent figure in a related field (e.g. sentic computing, SA-related linked data, etc.), followed by presentations of either full or position papers, demos, and general discussion. A session will be dedicated to a summary of the Concept-based Sentiment Analysis Challenge that is separately organized in the same ESWC2014 Conference.

==Submissions==

Submissions must comply with the Springer LNCS style and will be made using [https://www.easychair.org/conferences/?conf=ssa14 EasyChair].

Authors are invited to submit:

* Full papers (up to 8 pages)
* Short and position papers (up to 4 pages)

Accepted papers will be published by 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 proceedings.

==Important dates==

* <strike>Submission deadline: March 6, 2014 </strike>
* <strike>Submission deadline (Extended): March 10, 2014</strike>
* Submission deadline (Extended but abstract needed ASAP): March 14, 2014
* Notifications: April 1, 2014
* Camera ready version: April 15, 2014

==Workshop chairs:==

* Aldo Gangemi (contact person), U. Paris Nord France/ISTC-CNR Rome Italy, aldo.gangemi@lipn.univ-paris13.fr, http://istc.cnr.it/people/aldo-gangemi
* Harith Alani, KMI-OU Milton Keynes UK, h.alani@open.ac.uk, http://people.kmi.open.ac.uk/harith/
* Malvina Nissim, University of Bologna, malvina.nissim@unibo.it http://corpora.ficlit.unibo.it/People/Nissim/index.html
* Erik Cambria, NUS Singapore, cambria@nus.edu.sg, http://sentic.net
* Diego Reforgiato, ISTC-CNR Catania Italy, diego.reforgiato@istc.cnr.it, http://www.istc.cnr.it/people/diego-reforgiato-recupero

==Program Committee:==

* Valerio Basile, Rijksuniversiteit Groningen, The Netherlands
* Paul Buitelaar, National University of Ireland, Galway, Ireland
* Davide Buscaldi, University Paris Nord, France
* Catherine Havasi, MIT Boston, USA
* Chenghua Lin, University of Aberdeen, UK
* Paolo Rosso, Universitat Politècnica de València, Spain
* Hassan Saif, KMi, OU
* J. Fernando Sánchez-Rada, Universidad Politécnica de Madrid, Spain
* Verónica Perez Rosas, University of North-Texas, USA
* Bebo White, University of Stanford, USA

==Relevant References==

# Sentiment 140. http://www.sentiment140.com/.
# Opinion Crawl. http://opinioncrawl.com/.
# Social Mention. http://www.socialmention.com/.
# Chenghua Lin, Yulan He, Richard Everson, and Stefan Ruger. Weakly supervised joint sentiment-topic detection from text. IEEE Transactions on Knowledge and Data Engineering, 24(6):1134–1145, 2012.
# Keke Cai, Scott Spangler, Ying Chen, and Li Zhang. Leveraging sentiment analysis for topic detection. Web Intelli. and Agent Sys., 8(3):291–302, August 2010.
# Ivan Titov and Ryan McDonald. Modeling online reviews with multi- grain topic models. In Proceedings of the 17th international conference on World Wide Web, WWW ’08, pages 111–120, New York, NY, USA, 2008. ACM.
# Richard Johansson and Alessandro Moschitti. Relational features in fine-grained opinion analysis. Computational Linguistics, 39(3):473–509, 2013.
# Aldo Gangemi, Valentina Presutti, Diego Reforgiato Recupero. Frame-based detection of opinion holders and topics: a model and a tool. IEEE Computational Intelligence, 9(1), 2014.
# E. Cambria and A. Hussain. Sentic computing: Techniques, tools, and applications. Dordrecht, Netherlands: Springer, ISBN: 978-94-007- 5069-2, 2012.
# Bing Liu (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers.
# Saif, Hassan; He, Yulan and Alani, Harith (2012). Semantic sentiment analysis of Twitter. In: The 11th International Semantic Web Conference (ISWC 2012), 11-15 November 2012, Boston, MA, USA.

==Workshop Schedule==
Check [http://stlab.istc.cnr.it/software/SSA14-ESWC/ScheduleWorkshop.png here]
[[Media:http://stlab.istc.cnr.it/software/SSA14-ESWC/ScheduleWorkshop.png]]
{{Event
|Name=Semantic Sentiment Analysis 2014
|HasStartDate=2014/05/25
|HasEndDate=2014/05/25
}}
{{Submission deadline
|deadline=2014/03/06
|expired=no
|intime=6 March, 2014
}}

SemanticSentimentAnalysis2014

2014-05-20T07:58:18Z

Diegoreforgiato:

[[Category:Event]]

Semantic Sentiment Analysis 2014

The First Workshop on Semantic Sentiment Analysis at [http://2014.eswc-conferences.org/ ESWC2014], Hersonissos, Crete, 25 May, 2014

==Semantic Web and Sentiment Analysis==

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 exponentially.

The opportunity to capture the sentiment of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing prediction. However, the distillation of knowledge from such a large amount of unstructured information, is so difficult that hybridizing different methods from complementary disciplines facing similar challenges is a key activity.

Various Natural Language Processing techniques have been applied to process texts to detect subjective statements and their sentiment. This task is known as sentiment analysis, and overlaps with opinion mining.
Sentiment analysis over social media faces several challenges due to informal language, uncommon abbreviations, condensed text, ambiguity, illusive context, etc. Much work in recent years focused on investigating new methods for overcoming these problems to increase sentiment analysis accuracy over Twitter and the like.

Semantics can play an important role in enhancing our ability to accurately monitor sentiment over social media with respect to specific concept and topics. For example, using semantics will enable us to extract and distinguish sentiment about, say Berlusconi, in politics, business, criminal investigations, football, or for different events that involves him.
When moving from one context to another, or from one event to another, opinions can shift from positive to negative, or neutral. Semantics can capture this evolution and to differentiate its results accordingly, whereas most existing sentiment analysis systems provide an analysis that can be too coarse-grained, due to missed contextualization.

Mining opinions and sentiments from natural language is a difficult task as opinions and sentiments are often conveyed implicitly through latent semantics, which make approaches based on surface lexical hints less effective in those cases. To this end, semantic sentiment analysis may go beyond word-level analysis of text, and provide novel approaches to opinion mining and sentiment analysis that allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.

Semantic sentiment analysis may benefit from the use of web ontologies (e.g. linked data, multilingual linked lexica, linked vocabularies), or graph-based quasi-ontologies (e.g. ConceptNet, SenticNet, Nell, OIE, etc.) which enable the aggregation of conceptual and affective information associated with natural language opinions.

This quick overview already gives room to the role of a founded, semantic sentiment analysis, capable of distinguishing the different tasks and roles involved in features, methods, resources, and tools for sentiment analysis, and opening to the rich background knowledge that exists, or can be generated and linked within the semantic web.

Therefore, this workshop focuses on the introduction, presentation, and discussion of novel approaches to semantic sentiment analysis. Special focus will be given to semantic methods, models, and tools that exploit common-sense knowledge bases to perform multi- or open-domain sentiment analysis.

The intended audience of the workshop includes researchers from academia and industry as well as professionals and industrial practitioners to discuss and exchange positions on new hybrid techniques, which use semantics for sentiment analysis. The expected number of participants ranges between 20 and 40.

== Topics of Interest ==
Includes but not limited to:
* Ontologies and knowledge bases for sentiment analysis
* Topic and entity based sentiment analysis
* Evolution of sentiment within and across social media systems and topics
* Entity-based sentiment analysis
* Semantic processing of social media for sentiment analysis
* Contextualised sentiment analysis
* Comparison of semantic approaches for sentiment analysis
* Personalised sentiment analysis and monitoring
* Prediction of sentiment towards events, people, organisations, etc.
* Baselines and datasets for semantic sentiment analysis

==Workshop format==

This half-day workshop will consist in an invited lecture by a prominent figure in a related field (e.g. sentic computing, SA-related linked data, etc.), followed by presentations of either full or position papers, demos, and general discussion. A session will be dedicated to a summary of the Concept-based Sentiment Analysis Challenge that is separately organized in the same ESWC2014 Conference.

==Submissions==

Submissions must comply with the Springer LNCS style and will be made using [https://www.easychair.org/conferences/?conf=ssa14 EasyChair].

Authors are invited to submit:

* Full papers (up to 8 pages)
* Short and position papers (up to 4 pages)

Accepted papers will be published by 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 proceedings.

==Important dates==

* <strike>Submission deadline: March 6, 2014 </strike>
* <strike>Submission deadline (Extended): March 10, 2014</strike>
* Submission deadline (Extended but abstract needed ASAP): March 14, 2014
* Notifications: April 1, 2014
* Camera ready version: April 15, 2014

==Workshop chairs:==

* Aldo Gangemi (contact person), U. Paris Nord France/ISTC-CNR Rome Italy, aldo.gangemi@lipn.univ-paris13.fr, http://istc.cnr.it/people/aldo-gangemi
* Harith Alani, KMI-OU Milton Keynes UK, h.alani@open.ac.uk, http://people.kmi.open.ac.uk/harith/
* Malvina Nissim, University of Bologna, malvina.nissim@unibo.it http://corpora.ficlit.unibo.it/People/Nissim/index.html
* Erik Cambria, NUS Singapore, cambria@nus.edu.sg, http://sentic.net
* Diego Reforgiato, ISTC-CNR Catania Italy, diego.reforgiato@istc.cnr.it, http://www.istc.cnr.it/people/diego-reforgiato-recupero

==Program Committee:==

* Valerio Basile, Rijksuniversiteit Groningen, The Netherlands
* Paul Buitelaar, National University of Ireland, Galway, Ireland
* Davide Buscaldi, University Paris Nord, France
* Catherine Havasi, MIT Boston, USA
* Chenghua Lin, University of Aberdeen, UK
* Paolo Rosso, Universitat Politècnica de València, Spain
* Hassan Saif, KMi, OU
* J. Fernando Sánchez-Rada, Universidad Politécnica de Madrid, Spain
* Verónica Perez Rosas, University of North-Texas, USA
* Bebo White, University of Stanford, USA

==Relevant References==

# Sentiment 140. http://www.sentiment140.com/.
# Opinion Crawl. http://opinioncrawl.com/.
# Social Mention. http://www.socialmention.com/.
# Chenghua Lin, Yulan He, Richard Everson, and Stefan Ruger. Weakly supervised joint sentiment-topic detection from text. IEEE Transactions on Knowledge and Data Engineering, 24(6):1134–1145, 2012.
# Keke Cai, Scott Spangler, Ying Chen, and Li Zhang. Leveraging sentiment analysis for topic detection. Web Intelli. and Agent Sys., 8(3):291–302, August 2010.
# Ivan Titov and Ryan McDonald. Modeling online reviews with multi- grain topic models. In Proceedings of the 17th international conference on World Wide Web, WWW ’08, pages 111–120, New York, NY, USA, 2008. ACM.
# Richard Johansson and Alessandro Moschitti. Relational features in fine-grained opinion analysis. Computational Linguistics, 39(3):473–509, 2013.
# Aldo Gangemi, Valentina Presutti, Diego Reforgiato Recupero. Frame-based detection of opinion holders and topics: a model and a tool. IEEE Computational Intelligence, 9(1), 2014.
# E. Cambria and A. Hussain. Sentic computing: Techniques, tools, and applications. Dordrecht, Netherlands: Springer, ISBN: 978-94-007- 5069-2, 2012.
# Bing Liu (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers.
# Saif, Hassan; He, Yulan and Alani, Harith (2012). Semantic sentiment analysis of Twitter. In: The 11th International Semantic Web Conference (ISWC 2012), 11-15 November 2012, Boston, MA, USA.

==Workshop Schedule==
Check [http://stlab.istc.cnr.it/software/SSA14-ESWC/ScheduleWorkshop.png here]

{{Event
|Name=Semantic Sentiment Analysis 2014
|HasStartDate=2014/05/25
|HasEndDate=2014/05/25
}}
{{Submission deadline
|deadline=2014/03/06
|expired=no
|intime=6 March, 2014
}}

SemanticSentimentAnalysis2014

2014-05-20T07:57:59Z

Diegoreforgiato:

[[Category:Event]]

Semantic Sentiment Analysis 2014

The First Workshop on Semantic Sentiment Analysis at [http://2014.eswc-conferences.org/ ESWC2014], Hersonissos, Crete, 25 May, 2014

==Semantic Web and Sentiment Analysis==

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 exponentially.

The opportunity to capture the sentiment of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing prediction. However, the distillation of knowledge from such a large amount of unstructured information, is so difficult that hybridizing different methods from complementary disciplines facing similar challenges is a key activity.

Various Natural Language Processing techniques have been applied to process texts to detect subjective statements and their sentiment. This task is known as sentiment analysis, and overlaps with opinion mining.
Sentiment analysis over social media faces several challenges due to informal language, uncommon abbreviations, condensed text, ambiguity, illusive context, etc. Much work in recent years focused on investigating new methods for overcoming these problems to increase sentiment analysis accuracy over Twitter and the like.

Semantics can play an important role in enhancing our ability to accurately monitor sentiment over social media with respect to specific concept and topics. For example, using semantics will enable us to extract and distinguish sentiment about, say Berlusconi, in politics, business, criminal investigations, football, or for different events that involves him.
When moving from one context to another, or from one event to another, opinions can shift from positive to negative, or neutral. Semantics can capture this evolution and to differentiate its results accordingly, whereas most existing sentiment analysis systems provide an analysis that can be too coarse-grained, due to missed contextualization.

Mining opinions and sentiments from natural language is a difficult task as opinions and sentiments are often conveyed implicitly through latent semantics, which make approaches based on surface lexical hints less effective in those cases. To this end, semantic sentiment analysis may go beyond word-level analysis of text, and provide novel approaches to opinion mining and sentiment analysis that allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.

Semantic sentiment analysis may benefit from the use of web ontologies (e.g. linked data, multilingual linked lexica, linked vocabularies), or graph-based quasi-ontologies (e.g. ConceptNet, SenticNet, Nell, OIE, etc.) which enable the aggregation of conceptual and affective information associated with natural language opinions.

This quick overview already gives room to the role of a founded, semantic sentiment analysis, capable of distinguishing the different tasks and roles involved in features, methods, resources, and tools for sentiment analysis, and opening to the rich background knowledge that exists, or can be generated and linked within the semantic web.

Therefore, this workshop focuses on the introduction, presentation, and discussion of novel approaches to semantic sentiment analysis. Special focus will be given to semantic methods, models, and tools that exploit common-sense knowledge bases to perform multi- or open-domain sentiment analysis.

The intended audience of the workshop includes researchers from academia and industry as well as professionals and industrial practitioners to discuss and exchange positions on new hybrid techniques, which use semantics for sentiment analysis. The expected number of participants ranges between 20 and 40.

== Topics of Interest ==
Includes but not limited to:
* Ontologies and knowledge bases for sentiment analysis
* Topic and entity based sentiment analysis
* Evolution of sentiment within and across social media systems and topics
* Entity-based sentiment analysis
* Semantic processing of social media for sentiment analysis
* Contextualised sentiment analysis
* Comparison of semantic approaches for sentiment analysis
* Personalised sentiment analysis and monitoring
* Prediction of sentiment towards events, people, organisations, etc.
* Baselines and datasets for semantic sentiment analysis

==Workshop format==

This half-day workshop will consist in an invited lecture by a prominent figure in a related field (e.g. sentic computing, SA-related linked data, etc.), followed by presentations of either full or position papers, demos, and general discussion. A session will be dedicated to a summary of the Concept-based Sentiment Analysis Challenge that is separately organized in the same ESWC2014 Conference.

==Submissions==

Submissions must comply with the Springer LNCS style and will be made using [https://www.easychair.org/conferences/?conf=ssa14 EasyChair].

Authors are invited to submit:

* Full papers (up to 8 pages)
* Short and position papers (up to 4 pages)

Accepted papers will be published by 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 proceedings.

==Important dates==

* <strike>Submission deadline: March 6, 2014 </strike>
* <strike>Submission deadline (Extended): March 10, 2014</strike>
* Submission deadline (Extended but abstract needed ASAP): March 14, 2014
* Notifications: April 1, 2014
* Camera ready version: April 15, 2014

==Workshop chairs:==

* Aldo Gangemi (contact person), U. Paris Nord France/ISTC-CNR Rome Italy, aldo.gangemi@lipn.univ-paris13.fr, http://istc.cnr.it/people/aldo-gangemi
* Harith Alani, KMI-OU Milton Keynes UK, h.alani@open.ac.uk, http://people.kmi.open.ac.uk/harith/
* Malvina Nissim, University of Bologna, malvina.nissim@unibo.it http://corpora.ficlit.unibo.it/People/Nissim/index.html
* Erik Cambria, NUS Singapore, cambria@nus.edu.sg, http://sentic.net
* Diego Reforgiato, ISTC-CNR Catania Italy, diego.reforgiato@istc.cnr.it, http://www.istc.cnr.it/people/diego-reforgiato-recupero

==Program Committee:==

* Valerio Basile, Rijksuniversiteit Groningen, The Netherlands
* Paul Buitelaar, National University of Ireland, Galway, Ireland
* Davide Buscaldi, University Paris Nord, France
* Catherine Havasi, MIT Boston, USA
* Chenghua Lin, University of Aberdeen, UK
* Paolo Rosso, Universitat Politècnica de València, Spain
* Hassan Saif, KMi, OU
* J. Fernando Sánchez-Rada, Universidad Politécnica de Madrid, Spain
* Verónica Perez Rosas, University of North-Texas, USA
* Bebo White, University of Stanford, USA

==Relevant References==

# Sentiment 140. http://www.sentiment140.com/.
# Opinion Crawl. http://opinioncrawl.com/.
# Social Mention. http://www.socialmention.com/.
# Chenghua Lin, Yulan He, Richard Everson, and Stefan Ruger. Weakly supervised joint sentiment-topic detection from text. IEEE Transactions on Knowledge and Data Engineering, 24(6):1134–1145, 2012.
# Keke Cai, Scott Spangler, Ying Chen, and Li Zhang. Leveraging sentiment analysis for topic detection. Web Intelli. and Agent Sys., 8(3):291–302, August 2010.
# Ivan Titov and Ryan McDonald. Modeling online reviews with multi- grain topic models. In Proceedings of the 17th international conference on World Wide Web, WWW ’08, pages 111–120, New York, NY, USA, 2008. ACM.
# Richard Johansson and Alessandro Moschitti. Relational features in fine-grained opinion analysis. Computational Linguistics, 39(3):473–509, 2013.
# Aldo Gangemi, Valentina Presutti, Diego Reforgiato Recupero. Frame-based detection of opinion holders and topics: a model and a tool. IEEE Computational Intelligence, 9(1), 2014.
# E. Cambria and A. Hussain. Sentic computing: Techniques, tools, and applications. Dordrecht, Netherlands: Springer, ISBN: 978-94-007- 5069-2, 2012.
# Bing Liu (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers.
# Saif, Hassan; He, Yulan and Alani, Harith (2012). Semantic sentiment analysis of Twitter. In: The 11th International Semantic Web Conference (ISWC 2012), 11-15 November 2012, Boston, MA, USA.

==Workshop Schedule==
Check [http://stlab.istc.cnr.it/software/SSA14-ESWC/ScheduleWorkshop.png here]

Image:http://stlab.istc.cnr.it/software/SSA14-ESWC/ScheduleWorkshop.png|NeOn project
{{Event
|Name=Semantic Sentiment Analysis 2014
|HasStartDate=2014/05/25
|HasEndDate=2014/05/25
}}
{{Submission deadline
|deadline=2014/03/06
|expired=no
|intime=6 March, 2014
}}

2014-05-20T07:50:45Z

Diegoreforgiato:

[[Category:Event]]

Semantic Sentiment Analysis 2014

The First Workshop on Semantic Sentiment Analysis at [http://2014.eswc-conferences.org/ ESWC2014], Hersonissos, Crete, 25 May, 2014

==Semantic Web and Sentiment Analysis==

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 exponentially.

The opportunity to capture the sentiment of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing prediction. However, the distillation of knowledge from such a large amount of unstructured information, is so difficult that hybridizing different methods from complementary disciplines facing similar challenges is a key activity.

Various Natural Language Processing techniques have been applied to process texts to detect subjective statements and their sentiment. This task is known as sentiment analysis, and overlaps with opinion mining.
Sentiment analysis over social media faces several challenges due to informal language, uncommon abbreviations, condensed text, ambiguity, illusive context, etc. Much work in recent years focused on investigating new methods for overcoming these problems to increase sentiment analysis accuracy over Twitter and the like.

Semantics can play an important role in enhancing our ability to accurately monitor sentiment over social media with respect to specific concept and topics. For example, using semantics will enable us to extract and distinguish sentiment about, say Berlusconi, in politics, business, criminal investigations, football, or for different events that involves him.
When moving from one context to another, or from one event to another, opinions can shift from positive to negative, or neutral. Semantics can capture this evolution and to differentiate its results accordingly, whereas most existing sentiment analysis systems provide an analysis that can be too coarse-grained, due to missed contextualization.

Mining opinions and sentiments from natural language is a difficult task as opinions and sentiments are often conveyed implicitly through latent semantics, which make approaches based on surface lexical hints less effective in those cases. To this end, semantic sentiment analysis may go beyond word-level analysis of text, and provide novel approaches to opinion mining and sentiment analysis that allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.

Semantic sentiment analysis may benefit from the use of web ontologies (e.g. linked data, multilingual linked lexica, linked vocabularies), or graph-based quasi-ontologies (e.g. ConceptNet, SenticNet, Nell, OIE, etc.) which enable the aggregation of conceptual and affective information associated with natural language opinions.

This quick overview already gives room to the role of a founded, semantic sentiment analysis, capable of distinguishing the different tasks and roles involved in features, methods, resources, and tools for sentiment analysis, and opening to the rich background knowledge that exists, or can be generated and linked within the semantic web.

Therefore, this workshop focuses on the introduction, presentation, and discussion of novel approaches to semantic sentiment analysis. Special focus will be given to semantic methods, models, and tools that exploit common-sense knowledge bases to perform multi- or open-domain sentiment analysis.

The intended audience of the workshop includes researchers from academia and industry as well as professionals and industrial practitioners to discuss and exchange positions on new hybrid techniques, which use semantics for sentiment analysis. The expected number of participants ranges between 20 and 40.

== Topics of Interest ==
Includes but not limited to:
* Ontologies and knowledge bases for sentiment analysis
* Topic and entity based sentiment analysis
* Evolution of sentiment within and across social media systems and topics
* Entity-based sentiment analysis
* Semantic processing of social media for sentiment analysis
* Contextualised sentiment analysis
* Comparison of semantic approaches for sentiment analysis
* Personalised sentiment analysis and monitoring
* Prediction of sentiment towards events, people, organisations, etc.
* Baselines and datasets for semantic sentiment analysis

==Workshop format==

This half-day workshop will consist in an invited lecture by a prominent figure in a related field (e.g. sentic computing, SA-related linked data, etc.), followed by presentations of either full or position papers, demos, and general discussion. A session will be dedicated to a summary of the Concept-based Sentiment Analysis Challenge that is separately organized in the same ESWC2014 Conference.

==Submissions==

Submissions must comply with the Springer LNCS style and will be made using [https://www.easychair.org/conferences/?conf=ssa14 EasyChair].

Authors are invited to submit:

* Full papers (up to 8 pages)
* Short and position papers (up to 4 pages)

Accepted papers will be published by 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 proceedings.

==Important dates==

* <strike>Submission deadline: March 6, 2014 </strike>
* <strike>Submission deadline (Extended): March 10, 2014</strike>
* Submission deadline (Extended but abstract needed ASAP): March 14, 2014
* Notifications: April 1, 2014
* Camera ready version: April 15, 2014

==Workshop chairs:==

* Aldo Gangemi (contact person), U. Paris Nord France/ISTC-CNR Rome Italy, aldo.gangemi@lipn.univ-paris13.fr, http://istc.cnr.it/people/aldo-gangemi
* Harith Alani, KMI-OU Milton Keynes UK, h.alani@open.ac.uk, http://people.kmi.open.ac.uk/harith/
* Malvina Nissim, University of Bologna, malvina.nissim@unibo.it http://corpora.ficlit.unibo.it/People/Nissim/index.html
* Erik Cambria, NUS Singapore, cambria@nus.edu.sg, http://sentic.net
* Diego Reforgiato, ISTC-CNR Catania Italy, diego.reforgiato@istc.cnr.it, http://www.istc.cnr.it/people/diego-reforgiato-recupero

==Program Committee:==

* Valerio Basile, Rijksuniversiteit Groningen, The Netherlands
* Paul Buitelaar, National University of Ireland, Galway, Ireland
* Davide Buscaldi, University Paris Nord, France
* Catherine Havasi, MIT Boston, USA
* Chenghua Lin, University of Aberdeen, UK
* Paolo Rosso, Universitat Politècnica de València, Spain
* Hassan Saif, KMi, OU
* J. Fernando Sánchez-Rada, Universidad Politécnica de Madrid, Spain
* Verónica Perez Rosas, University of North-Texas, USA
* Bebo White, University of Stanford, USA

==Relevant References==

# Sentiment 140. http://www.sentiment140.com/.
# Opinion Crawl. http://opinioncrawl.com/.
# Social Mention. http://www.socialmention.com/.
# Chenghua Lin, Yulan He, Richard Everson, and Stefan Ruger. Weakly supervised joint sentiment-topic detection from text. IEEE Transactions on Knowledge and Data Engineering, 24(6):1134–1145, 2012.
# Keke Cai, Scott Spangler, Ying Chen, and Li Zhang. Leveraging sentiment analysis for topic detection. Web Intelli. and Agent Sys., 8(3):291–302, August 2010.
# Ivan Titov and Ryan McDonald. Modeling online reviews with multi- grain topic models. In Proceedings of the 17th international conference on World Wide Web, WWW ’08, pages 111–120, New York, NY, USA, 2008. ACM.
# Richard Johansson and Alessandro Moschitti. Relational features in fine-grained opinion analysis. Computational Linguistics, 39(3):473–509, 2013.
# Aldo Gangemi, Valentina Presutti, Diego Reforgiato Recupero. Frame-based detection of opinion holders and topics: a model and a tool. IEEE Computational Intelligence, 9(1), 2014.
# E. Cambria and A. Hussain. Sentic computing: Techniques, tools, and applications. Dordrecht, Netherlands: Springer, ISBN: 978-94-007- 5069-2, 2012.
# Bing Liu (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers.
# Saif, Hassan; He, Yulan and Alani, Harith (2012). Semantic sentiment analysis of Twitter. In: The 11th International Semantic Web Conference (ISWC 2012), 11-15 November 2012, Boston, MA, USA.

==Workshop Schedule==
![alternate text](http://stlab.istc.cnr.it/software/SSA14-ESWC/ScheduleWorkshop.png)
(http://stlab.istc.cnr.it/software/SSA14-ESWC/ScheduleWorkshop.png)
!(http://stlab.istc.cnr.it/software/SSA14-ESWC/ScheduleWorkshop.png)
{{Event
|Name=Semantic Sentiment Analysis 2014
|HasStartDate=2014/05/25
|HasEndDate=2014/05/25
}}
{{Submission deadline
|deadline=2014/03/06
|expired=no
|intime=6 March, 2014
}}