SemanticSentimentAnalysis2014

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Semantic Sentiment Analysis 2014
Semantic Sentiment Analysis 2014
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The First Workshop on Semantic Sentiment Analysis at ESWC2014
+
The First Workshop on Semantic Sentiment Analysis at [http://2014.eswc-conferences.org/ ESWC2014], Hersonissos, Crete, 25 May, 2014
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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.
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.
+
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.
-
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.  
+
-
Natural Language Processing techniques are widely used in order to process all sorts of texts – and especially those produced within social networks such as Twitter – so as to detect subjective statements and their sentiment [10]. This task is known as sentiment analysis, and overlaps with opinion mining. In currently available sentiment analysis systems such as [1, 2, 3], the user is typically prompted to insert a topic, on which the system returns a list of sentiment scores. However, some of the returned results are of limited use to the user because they are out of the domain the user is interested in. For example, one user could look for opinions about Berlusconi, and get several results related to Berlusconi in politics, business, criminal investigations, or football, or for different events Berlusconi is involved in. When moving from one context to another, or from one event to another, opinions can shift from positive to negative, or neutral.  
+
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.  
-
A sentiment analysis system should be able to differentiate its results accordingly, but most existing sentiment analysis systems provide an analysis that can be too coarse-grained, due to missed contextualization.
+
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.  
-
In fact, several works [4, 5, 6, 7, 8, 11) show that considering topics and entities jointly with sentiment features improves the performance of sentiment analysis systems.
+
-
Existing work (in social media in particular) also tends to focus on identifying sentiment of whole tweets rather than specific topics. Finally, the evolution and dynamics (how and where initiated, spread, flipped from + to -, etc) of sentiment over time are hardly considered.
+
-
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 SA, and opening to the rich background knowledge that exists, or can be generated and linked within the semantic web.
+
-
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 ineffective 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.
+
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.  
-
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.
+
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.
-
Combining NLP, lexical and semantic web approaches could provide us with the accuracy and strength of language processing techniques, as well as with the depth and breadth of semantic knowledge bases, through which also sentiments that are expressed in a subtle manner can be detected, as in the case of concepts that do not explicitly convey any emotion, but which are implicitly linked to other concepts that do so. What is challenging is the way techniques can be used and combined to yield highly performant systems.
+
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.  
-
With semantics, we can expand the current state of the art in sentiment analysis to be able to track, correlate, and compare sentiment of specific entities or group of related entities over time and across different contexts.
+
-
In brief, Sentiment Analysis techniques mainly count on NLP and lexical resources, and, as seen above, the current state of art targets lightweight analyses of sentences or documents.  
+
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.
-
Besides the companion field of opinion mining, another related field that can benefit from (and contribute to) this cross-disciplinary work is sentic computing (9), which also aims at abridging word-level natural language data and concept-level 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.  
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.
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
-
==References==
 
-
#[1] Sentiment 140. http://www.sentiment140.com/.
+
==Workshop format==
-
#[2] Opinion Crawl. http://opinioncrawl.com/.
+
-
#[3] Social Mention. http://www.socialmention.com/.
+
-
#[4] 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.
+
-
#[5] 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.
+
-
#[6] 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.
+
-
#[7] Richard Johansson and Alessandro Moschitti. Relational features in fine-grained opinion analysis. Computational Linguistics, 39(3):473–509, 2013.
+
-
#[8] 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.
+
-
#[9] E. Cambria and A. Hussain. Sentic computing: Techniques, tools, and applications. Dordrecht, Netherlands: Springer, ISBN: 978-94-007- 5069-2, 2012.
+
-
#[10] Bing Liu (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers.
+
-
#[11] 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.
+
 +
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.
-
==Workshop format==
 
-
The 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].
-
An indication of whether the workshop should be considered for a half-day or full-day event.
+
Authors are invited to submit:
-
Half-day
+
-
Related Workshops and Conferences
+
* Full papers (up to 8 pages)
-
This is a new workshop, the first time in a Semantic Web-related event (whereas workshops on pure NLP-based sentiment analysis are now regular in ACL and IJCAI conferences).
+
* Short and position papers (up to 4 pages)
 +
Accepted papers will be published by CEUR--WS.
-
==Workshop organizers/chairs==
+
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==
-
===Workshop chairs:===
+
* <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
-
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
+
==Workshop chairs:==
-
Aldo Gangemi is full professor at LIPN, University Paris 13 (Sorbonne Cité, CNRS UMR 7030), and researcher at ISTC-CNR, Rome. He has founded and directed the Semantic Technology Lab of ISTC-CNR since 2008. His research focuses on Semantic Technologies as an integration of methods from Knowledge Engineering, the Semantic Web, Linked Data, and Natural Language Processing. Applications domains include Medicine, Law, eGovernment, Agriculture and Fishery, Business, and Cultural Heritage. He has published more than 150 papers in international peer-reviewed journals, conferences and books, and seats as chair or committee member of international journals (Applied Ontology, Semantic Web), conferences (FOIS1998, LREC2006, EKAW2008), and in advisory committees for international organizations (SEMIC, ECDC). He has worked in the EU projects: Galen, WonderWeb, OntoWeb, Metokis, NeOn, BONy, and IKS. Some of his software projects (e.g. FRED, Aemoo, and Semantic Scout, Sentilo) in ontology engineering, knowledge extraction and exploratory search are demonstrated in web applications available from http://wit.istc.cnr.it/stlab-tools.
+
-
He has co-chaired the following workshops organized jointly with international conferences: LegOnt2003 (Third International Workshop on Legal Ontologies, at International Conference on AI&Law, Edinburgh, 2003), OntoLex2004, Second International Workshop on Ontologies and Lexica, at LREC2004, Lisbon, 2004, WORM04 (Second International Workshop on Regulatory Ontologies, at ODBASE Conference, Cyprus, 2004), CORONT04 (First International Workshop on Core Ontologies, at EKAW2004, Northampton, 2004), WORM05 (Third International Workshop on Regulatory Ontologies, at ODBASE Conference, Cyprus, 2005), OPSW05 (Ontology Engineering Patterns, at ISWC2005, Galway, Ireland, 2005), EON06 (Ontology Evaluation Workshop at WWW2006, Edinburgh, 2006), OntoLex (The ontology/lexicon interface workshop, Busan, Corea, 2007), KRSSW2008 (Workshop on Knowledge Reengineering on the Semantic Web at ESWC2008, Tenerife, Spain, 2008), SPIM2010 (Semantic Personalized Information Management at LREC2010, Malta, 2010), SPIM2011 (Semantic Personalized Information Management at ISWC2011, Bonn, 2011), WOP2012 (Workshop on Ontology Patterns at ISWC2012, Boston).
+
-
Harith Alani, KMI-OU Milton Keynes UK, h.alani@open.ac.uk, http://people.kmi.open.ac.uk/harith/
+
* 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
-
Dr Harith Alani is a senior lecturer at the Knowledge Media institute, The Open University, where he is heading a group specialising in Social Semantics and Web Science. Dr Alani has published more than 80 articles in various top class journals and conferences, and has been involved as a principle investigator in several European and national research projects. Dr Alani is a frequent member of organisational committees of leading conferences. He was a chair for the Semantic Data track at Hypertext 2012, Semantic Web In-Use track at ISWC 2011, and the Sensor Web track for ESWC 2011. He will be programme co-chair of ISWC 2013 and WebSci 2013. Dr Alani's research interests include social semantics, web science, social computing, social media analysis, ontology searching and ranking, offline-online social network tracking and analysis, and eGovernment2.0.
+
* 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
-
Erik Cambria, NUS Singapore, cambria@nus.edu.sg, http://sentic.net
+
==Program Committee:==
-
Erik Cambria received his BEng and MEng with honours in Electronic Engineering from the University of Genova, in 2005 and 2008 respectively. In 2011, he was awarded my Ph.D. in Computing Science and Mathematics, following the completion of an industrial Cooperative Awards in Science and Engineering (CASE) research project, funded by the UK Engineering and Physical Sciences Research Council (EPSRC), which was born from the collaboration between the University of Stirling, the MIT Media Laboratory, and Sitekit Solutions Ltd. Today, he is a research scientist at the National University of Singapore (Cognitive Science Programme, Temasek Laboratories) and an associate researcher at the Massachusetts Institute of Technology (Synthetic Intelligence Project, Media Laboratory). His interests include AI, Semantic Web, KR, NLP, big social data analysis, affective and cognitive modeling, intention awareness, HCI, and e-health.
+
-
Erik Cambria is invited speaker/tutor in many international venues, e.g., IEEE SSCI, MICAI, and WWW, associate editor of Springer Cognitive Computation, and guest editor of leading AI journals, e.g., IEEE Computational Intelligence Magazine, Elsevier Neural Networks, and IEEE Intelligent Systems. Erik is also chair of several international conferences, e.g., Extreme Learning Machines (ELM) and Brain Inspired Cognitive Systems (BICS), and workshop series, e.g., ICDM SENTIRE and KDD WISDOM. He is a fellow of the Brain Sciences Foundation, the National Laboratory of Pattern Recognition (NLPR – Institute of Automation, Chinese Academy of Sciences), the National Taiwan University, Microsoft Research Asia, and HP Labs India.
+
-
Malvina Nissim, U. Bologna Italy, malvina.nissim@unibo.it, http://corpora.dslo.unibo.it/People/Nissim/
+
-
Malvina Nissim is a tenured researcher in computational linguistics at the University of Bologna. Her research focuses on the computational handling of several lexical semantics, discourse, and more generally language-based communication phenomena. Recently, she has worked on the annotation and automatic detection of modality and on applying Natural Language Processing techniques, combined with lexical resources, to sentiment analysis, especially in social media. She has been chair and organiser of an ESSLLI Student Session, and (co-)organiser of several workshops including a SemEval shared task. She graduated in Linguistics from the University of Pisa, and obtained her PhD in Linguistics from the University of Pavia. Before joining the University of Bologna she was a post-doc at the University of Edinburgh and at the Institute for Cognitive Science and Technologies in Rome.
+
-
Diego Reforgiato, ISTC-CNR Catania Italy, diego.reforgiato@istc.cnr.it, http://www.istc.cnr.it/people/diego-reforgiato-recupero
+
* Valerio Basile, Rijksuniversiteit Groningen, The Netherlands
-
Diego Reforgiato Recupero is a Post Doctoral Researcher at CNR, working within the ISTC-STLAB group on semantic web and natural language processing. He holds a double bachelor from the University of Catania in computer science and a doctoral degree from the Department of Computer Science of University of Naples Federico II. In 2005 he was awarded a 3 year Post Doc fellowship with the University of Maryland where he won the Computer World Horizon Award in the USA for the best research project on OASYS, an opinion analysis system commercialized by SentiMetrix. In 2008, he won a Marie Curie International Grant to fund a 3 year Post Doc fellowship with the Department of Electrical, Electronic, and Computer Science Engineering (DIEEI) at the University of Catania where he won the “Best Researcher Award 2012” at the University of Catania for a project about the development of a green router nearing commercialisation. In the same year he got to the winning podium of the “Startup Weekend” event held in Catania and was a winner of Telecom Italia Working Capital Award with the project “Green Home Gateway”. He is co-founder of SentiMetrix, where he served on the board of directors. He is a patent co-owner in the field of data mining and sentiment analysis (20100023311). Dr. Reforgiato is also a co-founder of R2M Solution, where he currently serves on the board of directors. In March 2013 he published his first paper on SCIENCE, “Toward a Green Internet”, where he is the only author. Dr. Reforgiato has research experience across a wide array of industrial and FP7 research projects. He is also the co-chair of the Sentiment Analysis challenge for the upcoming European Semantic Web Conference 2014.
+
* 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==
-
==Program Committee (to be completed):==
+
# 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.
-
;Paolo Rosso, U.P. Valencia
+
==Workshop Schedule==
-
;Harith Alani, KMI-OU Milton Keynes
+
Check [http://stlab.istc.cnr.it/software/SSA14-ESWC/ScheduleWorkshop.png here]
-
;Paul Buitelaar, DERI Galway
+
-
;Bebo White, U. Stanford
+
-
;Fabio Ciravegna, U. Sheffield
+
-
;Eneko Agirre, U. Basque Country
+
-
;Davide Buscaldi, U. Paris Nord
+
-
;Carlo Strapparava, FBK Trento
+
-
;J. Fernando Sánchez-Rada, U.P. Madrid
+
-
;Björn Schuller, Imperial College London
+
-
;Catherine Havasi, MIT Boston
+
-
;V. S. Subrahmanian, U. of Maryland
+
-
;William H. Hsu, U. Kansas State
+
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+
|HasStartDate=2014/05/25
-
|HasEndDate=2014/05/30
+
|HasEndDate=2014/05/25
}}
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{{Submission deadline
{{Submission deadline
-
|deadline=2014/03/15
+
|deadline=2014/03/06
|expired=no
|expired=no
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|intime=yes
+
|intime=6 March, 2014
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Current revision


Semantic Sentiment Analysis 2014

The First Workshop on Semantic Sentiment Analysis at ESWC2014, Hersonissos, Crete, 25 May, 2014


Contents

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 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

  • Submission deadline: March 6, 2014
  • Submission deadline (Extended): March 10, 2014
  • Submission deadline (Extended but abstract needed ASAP): March 14, 2014
  • Notifications: April 1, 2014
  • Camera ready version: April 15, 2014

Workshop chairs:

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

  1. Sentiment 140. http://www.sentiment140.com/.
  2. Opinion Crawl. http://opinioncrawl.com/.
  3. Social Mention. http://www.socialmention.com/.
  4. 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.
  5. 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.
  6. 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.
  7. Richard Johansson and Alessandro Moschitti. Relational features in fine-grained opinion analysis. Computational Linguistics, 39(3):473–509, 2013.
  8. 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.
  9. E. Cambria and A. Hussain. Sentic computing: Techniques, tools, and applications. Dordrecht, Netherlands: Springer, ISBN: 978-94-007- 5069-2, 2012.
  10. Bing Liu (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers.
  11. 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

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Semantic Sentiment Analysis 2014 | Start date: 2014/05/25 | End date: 2014/05/25


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