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Semantic Web-Based Social Media Analysis

  • Chapter
Transactions on Computational Collective Intelligence XXII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 9655))

Abstract

With the on growing usage of microblogging services, such as Twitter, millions of users share opinions daily on virtually everything. Making sense of this huge amount of data using sentiment and emotion analysis, can provide invaluable benefits to organizations trying to better understand what the public thinks about their services and products. While the vast majority of now-a-days researches are solely focusing on improving the algorithms used for sentiment and emotion evaluation, the present one underlines the benefits of using a semantic based approach for modeling the analysis’ results, the emotions and the social media specific concepts. By storing the results as structured data, the possibilities offered by semantic web technologies, such as inference and accessing the vast knowledge in Linked Open Data, can be fully exploited. The paper also presents a novel semantic social media analysis platform, which is able to properly emphasize the users’ complex feeling such as happiness, affection, surprise, anger or sadness.

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Notes

  1. 1.

    http://www.twitter.com.

  2. 2.

    http://linkeddata.org.

  3. 3.

    http://dublincore.org/.

  4. 4.

    http://xmlns.com/foaf/spec/.

  5. 5.

    http://sioc-project.org/.

  6. 6.

    http://www.w3.org/2003/01/geo/.

  7. 7.

    http://cassandra.apache.org/.

  8. 8.

    http://multiwordnet.fbk.eu/.

  9. 9.

    http://www.sentiment140.com/.

  10. 10.

    http://www.netlingo.com/smileys.php.

  11. 11.

    http://www.smileyontology.com/.

  12. 12.

    http://www.sananalytics.com/lab/twitter-sentiment/.

  13. 13.

    http://wiki.dbpedia.org/.

References

  1. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation, Valletta, pp. 1320–1326 (2010)

    Google Scholar 

  2. Delcea, C., Bradea, I., Paun, R., Scarlat, E.: How impressionable are you? - grey knowledge, groups and strategies in OSN. In: Núñez, M., Nguyen, N.T., Camacho, D., Trawiński, B. (eds.) ICCCI 2015. LNCS, vol. 9329, pp. 171–180. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24069-5_16

    Chapter  Google Scholar 

  3. Kontopoulos, E., Berberidis, C., Dergiades, T., Bassiliades, N.: Ontology-based sentiment analysis of Twitter posts. Expert Syst. Appl. 40, 4065–4074 (2013)

    Article  Google Scholar 

  4. Delcea, C., Cotfas, L.-A., Paun, R.: Understanding online social networks’ users – a Twitter approach. In: Hwang, D., Jung, J.J., Nguyen, N.-T. (eds.) ICCCI 2014. LNCS, vol. 8733, pp. 145–153. Springer, Heidelberg (2014)

    Google Scholar 

  5. Delcea, C., Cotfas, L.-A., Paun, R.: Grey social networks. In: Hwang, D., Jung, J.J., Nguyen, N.-T. (eds.) ICCCI 2014. LNCS, vol. 8733, pp. 125–134. Springer, Heidelberg (2014)

    Google Scholar 

  6. Torkildson, M.K., Starbird, K., Aragon, C.: Analysis and visualization of sentiment and emotion on crisis tweets. In: Luo, Y. (ed.) CDVE 2014. LNCS, vol. 8683, pp. 64–67. Springer, Heidelberg (2014)

    Google Scholar 

  7. Rill, S., Reinel, D., Scheidt, J., Zicari, R.V.: PoliTwi: Early detection of emerging political topics on Twitter and the impact on concept-level sentiment analysis. Knowl.-Based Syst. 69, 24–33 (2014)

    Article  Google Scholar 

  8. Delcea, C., Bradea, I., Paun, R., Friptu, A.: A Healthcare companies’ performance view through OSN. In: Barbucha, D., Nguyen, N.T., Batubara, J. (eds.) New Trends in Intelligent Information and Database Systems. SCI, vol. 598, pp. 333–342. Springer, Heidelberg (2015)

    Google Scholar 

  9. Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y., Ngo, D.C.L.: Text mining for market prediction: a systematic review. Expert Syst. Appl. 41, 7653–7670 (2014)

    Article  Google Scholar 

  10. Ghiassi, M., Skinner, J., Zimbra, D.: Twitter brand sentiment analysis: a hybrid system using n-gram analysis and dynamic artificial neural network. Expert Syst. Appl. 40, 6266–6282 (2013)

    Article  Google Scholar 

  11. Mostafa, M.M.: More than words: social networks’ text mining for consumer brand sentiments. Expert Syst. Appl. 40, 4241–4251 (2013)

    Article  Google Scholar 

  12. Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment strength detection for the social web. J. Am. Soc. Inf. Sci. Technol. 63, 163–173 (2012)

    Article  Google Scholar 

  13. Robaldo, L., Di Caro, L.: OpinionMining-ML. Comput. Stand. Interfaces 35, 454–469 (2013)

    Article  Google Scholar 

  14. Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5, 1093–1113 (2014)

    Article  Google Scholar 

  15. Tsytsarau, M., Palpanas, T.: Survey on mining subjective data on the web. Data Min. Knowl. Discov. 24, 478–514 (2012)

    Article  MATH  Google Scholar 

  16. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5, 1–167 (2012)

    Article  Google Scholar 

  17. Shadbolt, N., Berners-Lee, T., Hall, W.: The semantic web revisited. IEEE Intell. Syst. 21, 96–101 (2006)

    Article  Google Scholar 

  18. Borst, W.N.: Construction of engineering ontologies for knowledge sharing and reuse. Universiteit Twente (1997)

    Google Scholar 

  19. Roberts, K., Roach, M., Johnson, J.: EmpaTweet: annotating and detecting emotions on Twitter. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation, Istanbul, pp. 3806–3813 (2012)

    Google Scholar 

  20. Baggia, P., Burkhardt, F., Pelachaud, C., Peter, C., Zovato, E.: Emotion Markup Language (EmotionML) 1.0. http://www.w3.org/TR/emotionml/

  21. Ashimura, K., Baggia, P., Burkhardt, F., Oltramari, A., Peter, C., Zovato, E.: Vocabularies for EmotionML. http://www.w3.org/TR/2012/NOTE-emotion-voc-20120510/

  22. Westerski, A., Iglesias Fernandez, C.A., Tapia Rico, F.: Linked opinions: describing sentiments on the structured web of data. Presented at the 4th International Workshop Social Data on the Web, Bonn, Germany (2011)

    Google Scholar 

  23. Belhajjame, K., Cheney, J., Corsar, D., Garijo, D., Soiland-Reyes, S., Zednik, S., Zhao, J.: PROV-O: The PROV Ontology. http://www.w3.org/TR/prov-o/

  24. Hastings, J., Ceusters, W., Smith, B., Mulligan, K.: Dispositions and processes in the Emotion Ontology. In: Proceedings of the ICBO 2011 (2011)

    Google Scholar 

  25. Francisco, V., Hervás, R., Peinado, F., Gervás, P.: EmoTales: creating a corpus of folk tales with emotional annotations. Lang. Resour. Eval. 46, 341–381 (2012)

    Article  Google Scholar 

  26. Montejo-Ráez, A., Martínez-Cámara, E., Martín-Valdivia, M.T., Ureña-López, L.A.: Ranked WordNet graph for sentiment polarity classification in Twitter. Comput. Speech Lang. 28, 93–107 (2014)

    Article  Google Scholar 

  27. Baldoni, M., Baroglio, C., Patti, V., Rena, P.: From tags to emotions: ontology-driven sentiment analysis in the social semantic web. Presented at the Intelligenza Artificiale (2012)

    Google Scholar 

  28. Togias, K., Kameas, A.: An ontology-based representation of the Twitter REST API. Presented, November 2012

    Google Scholar 

  29. Cotfas, L.-A., Delcea, C., Roxin, I., Paun, R.: Twitter ontology-driven sentiment analysis. In: Barbucha, D., Nguyen, N.T., Batubara, J. (eds.) New Trends in Intelligent Information and Database Systems. SCI, vol. 598, pp. 131–139. Springer, Cham (2015)

    Google Scholar 

  30. Breslin, J.G., Passant, A., Decker, S.: The Social Semantic Web. Springer, Heidelberg (2009)

    Book  Google Scholar 

  31. Fornara, N., Ježić, G., Kušek, M., Lovrek, I., Podobnik, V., Tržec, K.: Semantics in multi-agent systems. In: Ossowski, S. (ed.) Agreement Technologies, pp. 115–136. Springer, Dordrecht (2013)

    Chapter  Google Scholar 

  32. Maynard, D., Bontcheva, K., Rout, D.: Challenges in developing opinion mining tools for social media. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation, Istanbul, pp. 15–22 (2012)

    Google Scholar 

  33. Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: the good the bad and the omg! In: Proceedings of the Fifth International Conference on Weblogs and Social Media, Barcelona, pp. 538–541 (2011)

    Google Scholar 

  34. Rabl, T., Gómez-Villamor, S., Sadoghi, M., Muntés-Mulero, V., Jacobsen, H.-A., Mankov-skii, S.: Solving big data challenges for enterprise application performance management. Proc. VLDB Endowment 5, 1724–1735 (2012)

    Article  Google Scholar 

  35. Carter, S., Weerkamp, W., Tsagkias, M.: Microblog language identification: overcoming the limitations of short, unedited and idiomatic text. Lang. Resour. Eval. 47, 195–215 (2013)

    Article  Google Scholar 

  36. Cavnar, W., Trenkle, J.: N-gram-based text categorization. In: Proceedings of the 3rd Annual Symposium on Document Analysis and Information Retrieval, SDAIR 1994 (1994)

    Google Scholar 

  37. Bao, Y., Quan, C., Wang, L., Ren, F.: The role of pre-processing in Twitter sentiment analysis. In: Huang, D.-S., Jo, K.-H., Wang, L. (eds.) ICIC 2014. LNCS, vol. 8589, pp. 615–624. Springer, Heidelberg (2014)

    Google Scholar 

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Acknowledgments

The study was produced as part of the SCOPANUM research project, supported by grants from CSFRS (http://csfrs.fr/), and a doctoral grant from Pays de Montbéliard Agglomération (http://www.agglo-montbeliard.fr/). The authors also acknowledge the support of Leverhulme Trust International Network research project “IN-2014-020”.

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Authors and Affiliations

  1. Franche-Comté University, Montbéliard, France

    Liviu-Adrian Cotfas, Antonin Segault & Ioan Roxin

  2. Bucharest University of Economic Studies, Bucharest, Romania

    Liviu-Adrian Cotfas & Camelia Delcea

Authors
  1. Liviu-Adrian Cotfas
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  2. Camelia Delcea
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  3. Antonin Segault
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  4. Ioan Roxin
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Correspondence to Liviu-Adrian Cotfas .

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Editors and Affiliations

  1. Wroclaw University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Hawthorn, Australia

    Ryszard Kowalczyk

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Cotfas, LA., Delcea, C., Segault, A., Roxin, I. (2016). Semantic Web-Based Social Media Analysis. In: Nguyen, N.T., Kowalczyk, R. (eds) Transactions on Computational Collective Intelligence XXII. Lecture Notes in Computer Science(), vol 9655. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49619-0_8

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  • DOI: https://doi.org/10.1007/978-3-662-49619-0_8

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