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Semantic Web Evaluation Challenge

SemWebEval 2014: Semantic Web Evaluation Challenge pp 3–20Cite as

ESWC’14 Challenge on Concept-Level Sentiment Analysis

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 475))

Abstract

With the introduction of social networks, blogs, wikis, etc., the users’ behavior and their interaction in the Web have changed. As a consequence, people express their opinions and sentiments in a totally different way with respect to the past. All this information hinders potential business opportunities, especially within the advertising world, and key stakeholders need to catch up with the latest technology if they want to be at the forefront in the market. In practical terms, the automatic analysis of online opinions involves a deep understanding of natural language text, and it has been proved that the use of semantics improves the accuracy of existing sentiment analysis systems based on classical machine learning or statistical approaches. To this end, the Concept Level Sentiment Analysis challenge aims to provide a push in this direction offering the researchers an event where they can learn new approaches for the employment of Semantic Web features within their systems of sentiment analysis bringing to better performance and higher accuracy. The challenge aims to go beyond a mere word-level analysis of text and provides novel methods to process opinion data from unstructured textual information to structured machine-processable data.

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Notes

  1. 1.

    http://challenges.2014.eswc-conferences.org/index.php/SemSA

  2. 2.

    http://sentic.net/

  3. 3.

    http://dbpedia.org

  4. 4.

    http://ontologydesignpatterns.org/wiki/SemanticSentimentAnalysis2014

  5. 5.

    http://alt.qcri.org/semeval2014/

  6. 6.

    Check http://iswc2014.semanticweb.org/ for the current edition

  7. 7.

    http://challenge.semanticweb.org/2013/submissions/

  8. 8.

    http://sentic.net/sentics/

  9. 9.

    http://www.cs.jhu.edu/~mdredze/datasets/sentiment/

  10. 10.

    http://sentic.net/eswc14.zip

  11. 11.

    http://en.wikipedia.org/wiki/Precision_and_recall

References

  1. Baccianella, A., Esuli, S., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Calzolari, N., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., Tapias, D. (eds.) Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC’10), Valletta, Malta (2010)

    Google Scholar 

  2. Bosco, C., Patti, V., Bolioli, A.: Developing corpora for sentiment analysis: the case of irony and Senti-TUT. IEEE Intell. Syst. 28(2), 55–63 (2013)

    Article  Google Scholar 

  3. Bradley, M., Lang, P.: Affective norms for English words (ANEW): stimuli, instruction manual and affective ratings. Technical report, The Center for Research in Psychophysiology, University of Florida (1999)

    Google Scholar 

  4. Cambria, E., Hussain, A.: Sentic Computing: Techniques, Tools, and Applications, vol. 2. Springer, Heidelberg (2012)

    Google Scholar 

  5. Cambria, E., Schuller, B., Xia, Y., Havasi, C.: New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 28(2), 15–21 (2013)

    Article  Google Scholar 

  6. Cambria, E., White, B.: Jumping NLP curves: a review of natural language processing research. IEEE Comput. Intell. Mag. 9(2), 48–57 (2014)

    Article  Google Scholar 

  7. Cambria, E., Olsher, D., Rajagopal, D.: Senticnet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Brodley, C.E., Stone, P. (eds.) Twenty-Eight AAAI Conference on Artificial Intelligence, pp. 1515–1521. AAAI Press, Palo Alto, July 2014

    Google Scholar 

  8. Chen, H., Wuand, Z., Cudré-Mauroux, P.: Semantic Web meets computational intelligence: state of the art and perspectives. IEEE Comput. Intell. Mag. 7(2), 67–74 (2012)

    Article  Google Scholar 

  9. Gangemi, A., Presutti, V., Reforgiato Recupero, D.: Frame-based detection of opinion holders and topics: a model and a tool. IEEE Comput. Intell. Mag. 9(1), 20–30 (2014)

    Google Scholar 

  10. Garcia-Moya, L., Anaya-Sanchez, H., Berlanga-Llavori, R.: Retrieving product features and opinions from customer reviews. IEEE Intell. Syst. 28(3), 19–27 (2013)

    Article  Google Scholar 

  11. Hung, C., Lin, H.-K.: Using objective words in sentiwordnet to improve word-of-mouth sentiment classification. IEEE Intell. Syst. 28(2), 47–54 (2013)

    Article  Google Scholar 

  12. Johansson, R., Moschitti, A.: Relational features in fine-grained opinion analysis. Comput. Ling. 39(3), 473–509 (2013)

    Article  Google Scholar 

  13. Liu, H., Singh, P.: Conceptnet: a practical commonsense reasoning toolkit. BT Technol. J. 22, 211–226 (2004)

    Article  Google Scholar 

  14. Poria, S., Gelbukh, A.F., Hussain, A., Howard, N., Das, D., Bandyopadhyay, S.: Enhanced senticnet with affective labels for concept-based opinion mining. IEEE Intell. Syst. 28(2), 31–38 (2013)

    Article  Google Scholar 

  15. Powers, D.M.W.: Evaluation: from precision, recall and F-factor to ROC, informedness, markedness & correlation. Technical report SIE-07-001, School of Informatics and Engineering, Flinders University, Adelaide, Australia (2007)

    Google Scholar 

  16. Presutti, V., Draicchio, F., Gangemi, A.: Knowledge extraction based on discourse representation theory and linguistic frames. In: ten Teije, A., Völker, J., Handschuh, S., Stuckenschmidt, H., d’Acquin, M., Nikolov, A., Aussenac-Gilles, N., Hernandez, N. (eds.) EKAW 2012. LNCS, vol. 7603, pp. 114–129. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  17. Reforgiato Recupero, D., Presutti, V., Consoli, S., Gangemi, A., Nuzzolese, A.: Sentilo: frame-based sentiment analysis. Cogn. Comput. (2014)

    Google Scholar 

  18. Saif, H., He, Y., Alani, H.: Semantic sentiment analysis of Twitter. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 508–524. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  19. Strapparava, C., Valitutti, A.: WordNet-affect: an affective extension of WordNet. In: LREC, Lisbon, pp. 1083–1086 (2004)

    Google Scholar 

  20. Tsai, A.C.-R., Wu, C.-E., Tsai, R.T.-H., Hsu, J.Y.-J.: Building a concept-level sentiment dictionary based on commonsense knowledge. IEEE Intell. Syst. 28(2), 22–30 (2013)

    Google Scholar 

  21. Weichselbraun, A., Gindl, S., Scharl, A.: Extracting and grounding context-aware sentiment lexicons. IEEE Intell. Syst. 28(2), 39–46 (2013)

    Article  Google Scholar 

  22. Weigand, E. (ed.).: Emotion in Dialogic Interaction. Current Issues in Linguistic Theory, vol. 248. John Benjamins, Philadelphia (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. CNR, Via Gaifami 18, 95028, Catania, Italy

    Diego Reforgiato Recupero

  2. School of Computer Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore, Singapore

    Erik Cambria

Authors
  1. Diego Reforgiato Recupero
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  2. Erik Cambria
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Corresponding author

Correspondence to Diego Reforgiato Recupero .

Editor information

Editors and Affiliations

  1. Semantic Technology Laboratory, ISTC-CNR, Rome, Italy

    Valentina Presutti

  2. Université Paris-Sorbonne,, Paris, France

    Milan Stankovic

  3. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

  4. Universidad Autónoma de Madrid, Madrid, Spain

    Iván Cantador

  5. University of Bologna, Bologna, Italy

    Angelo Di Iorio

  6. Polytechnic University of Bari, Bari, Italy

    Tommaso Di Noia

  7. University of Birmingham, Birmingham, United Kingdom

    Christoph Lange

  8. ISTC-CNR, Semantic Technology Laboratory, Rome, Italy

    Diego Reforgiato Recupero

  9. Elsevier B.V., Amsterdam, The Netherlands

    Anna Tordai

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© 2014 Springer International Publishing Switzerland

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Reforgiato Recupero, D., Cambria, E. (2014). ESWC’14 Challenge on Concept-Level Sentiment Analysis. In: Presutti, V., et al. Semantic Web Evaluation Challenge. SemWebEval 2014. Communications in Computer and Information Science, vol 475. Springer, Cham. https://doi.org/10.1007/978-3-319-12024-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-12024-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12023-2

  • Online ISBN: 978-3-319-12024-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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