Skip to main content

Spanish Corpus for Sentiment Analysis Towards Brands

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10458))

Abstract

Posts published in the social media are a good source of feedback to assess the impact of advertising campaigns. Whereas most of the published corpora of messages in the Sentiment Analysis domain tag posts with polarity labels, this paper presents a corpus in Spanish language where tagging has been made using 8 predefined emotions: love-hate, happiness-sadness, trust-fear, satisfaction-dissatisfaction. In every post, extracted from Twitter, sentiments have been annotated towards each specific brand under study. The corpus is published as a collection of RDF resources with links to external entities. Also a vocabulary describing this emotion classification along with other relevant aspects of customer’s opinion is provided.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    The SAB corpus is available online offering only the ID of the tweets.

References

  1. Baker, C.F., Fillmore, C.J., Lowe, J.B.: The Berkeley FrameNet project. In: Proceedings of the COLING-ACL, vol. 1, pp. 86–90. ACL (1998)

    Google Scholar 

  2. Baldoni, M., Baroglio, C., et al.: ArsEmotica: emotions in the social semantic web. In: Proceedings of the 7th International Conference on Semantic Systems, pp. 171–174 (2011)

    Google Scholar 

  3. Borden, N.H.: The concept of the marketing mix. J. Advertising Res. 4(2), 2–7 (1964)

    Google Scholar 

  4. Breslin, J.G., Decker, S., et al.: Sioc: an approach to connect web-based communities. Int. J. Web Based Communities 2(2), 133–142 (2006)

    Article  Google Scholar 

  5. Ciao, website with opinions on several topics. http://www.ciao.es/

  6. Corpus COAR, with opinions about restaurants. http://sinai.ujaen.es/coar/

  7. Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20(1), 37–46 (1960)

    Article  Google Scholar 

  8. Corpus HOpinion. http://clic.ub.edu/corpus/es/node/106

  9. 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). doi:10.1007/978-3-319-16211-9_14

    Google Scholar 

  10. Cruz, F.L., Troyano, J.A., et al.: Clasificación de documentos basada en la opinión: experimentos con un corpus de crıticas de cine en espanol. Procesamiento Lenguaje Nat. 41, 73–80 (2008)

    Google Scholar 

  11. Cumbreras, M.Á.G., Cámara, E.M., et al.: TASS 2015 - The evolution of the Spanish opinion mining systems. Procesamiento Lenguaje Nat. 56, 33–40 (2016)

    Google Scholar 

  12. DBPedia website. http://dbpedia.org/

  13. Dong, Z., Dong, Q., Hao, C.: Hownet and its computation of meaning. In: Proceedings of COLING 2010: Demonstrations, pp. 53–56. ACL (2010)

    Google Scholar 

  14. Ekman, P., Friesen, W.V., Ellsworth, P.: Emotion in the Human Face: Guidelines for Research and an Integration of Findings. Pergamon Press (1972)

    Google Scholar 

  15. Emotion ML. https://www.w3.org/TR/emotionml/

  16. Fleiss, J.L., Cohen, J.: The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educ. Psychol. Meas. 33(3), 613–619 (1973)

    Article  Google Scholar 

  17. Francisco, V., Gervás, P., Peinado, F.: Ontological reasoning to configure emotional voice synthesis. In: Marchiori, M., Pan, J.Z., Marie, C.S. (eds.) RR 2007. LNCS, vol. 4524, pp. 88–102. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72982-2_7

    Chapter  Google Scholar 

  18. Gangemi, A., Guarino, N., Masolo, C., Oltramari, A., Schneider, L.: Sweetening ontologies with DOLCE. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS, vol. 2473, pp. 166–181. Springer, Heidelberg (2002). doi:10.1007/3-540-45810-7_18

    Chapter  Google Scholar 

  19. Gil, R., Virgili-Gomá, J., et al.: Emotions ontology for collaborative modelling and learning of emotional responses. Comput. Hum. Behav. 51, 610–617 (2015)

    Article  Google Scholar 

  20. Grassi, M.: Developing HEO human emotions ontology. In: Fierrez, J., Ortega-Garcia, J., Esposito, A., Drygajlo, A., Faundez-Zanuy, M. (eds.) BioID 2009. LNCS, vol. 5707, pp. 244–251. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04391-8_32

    Chapter  Google Scholar 

  21. Hastings, J., Ceusters, W., et al.: The emotion ontology: enabling interdisciplinary research in the affective sciences. In: International and Interdisciplinary Conference on Modeling and Using Context, pp. 119–123 (2011)

    Google Scholar 

  22. Hastings, J., Ceusters, W., et al.: Annotating affective neuroscience data with the emotion ontology. In: Proceedings of the Workshop Towards an Ontology of Mental Functioning at ICBO, pp. 1–5 (2012)

    Google Scholar 

  23. Havas Media website. http://www.havasmedia.com/

  24. Hepp, M.: GoodRelations: an ontology for describing products and services offers on the web. In: Gangemi, A., Euzenat, J. (eds.) EKAW 2008. LNCS, vol. 5268, pp. 329–346. Springer, Heidelberg (2008). doi:10.1007/978-3-540-87696-0_29

    Chapter  Google Scholar 

  25. LPS BIGGER project website. http://www.cienlpsbigger.es/

  26. Martínez-Cámara, E., Martín-Valdivia, M.T., et al.: Polarity classification for Spanish tweets using the COST corpus. J. Inf. Sci. 41(3), 263–272 (2015)

    Article  Google Scholar 

  27. Masquemedicos, with opinions in the medical domain. http://masquemedicos.com

  28. Mohammad, S.M., Kiritchenko, S.: Using hashtags to capture fine emotion categories from tweets. Comput. Intell. 31(2), 301–326 (2015)

    Article  MathSciNet  Google Scholar 

  29. Molina-González, M.D., Martínez-Cámara, E., et al.: Cross-domain sentiment analysis using Spanish opinionated words. In: Métais, E., Roche, M., Teisseire, M. (eds.) NLDB 2014. LNCS, pp. 214–219. Springer, Cham (2014). doi:10.1007/978-3-319-07983-7_28

    Google Scholar 

  30. Montejo-Ráez, A., Díaz-Galiano, M.C., et al.: Crowd explicit sentiment analysis. Knowl. Based Syst. 69(1), 134–139 (2014)

    Article  Google Scholar 

  31. MuchoCine, Spanish website with reviews about films. www.muchocine.net

  32. Nakamura, A.: Kanjo Hyogen Jiten. Tokyodo Publishing (1993)

    Google Scholar 

  33. Nielsen: The social media report. http://blog.nielsen.com/nielsenwire/social/2012/

  34. Obrenovic, Z., Garay, N., López, J.M., Fajardo, I., Cearreta, I.: An ontology for description of emotional cues. In: Tao, J., Tan, T., Picard, R.W. (eds.) ACII 2005. LNCS, vol. 3784, pp. 505–512. Springer, Heidelberg (2005). doi:10.1007/11573548_65

    Chapter  Google Scholar 

  35. Plaza-Del-Arco, F.M., Martín-Valdivia, M.T., et al.: COPOS: Corpus of patient opinions in Spanish. Application of sentiment analysis techniques. Procesamiento Lenguaje Nat. 57, 83–90 (2016)

    Google Scholar 

  36. Plutchik, R.: The nature of emotions: Human emotions have deep evolutionary roots (2001)

    Google Scholar 

  37. Ptaszynski, M., Rzepka, R., et al.: A robust ontology of emotion objects. In: Proceedings of the 18th Annual Meeting of the Association for Natural Language Processing, pp. 719–722 (2012)

    Google Scholar 

  38. Rangel, F., Rosso, P., Reyes, A.: Emotions and irony per gender in facebook. In: Proceedings of Workshop ES3LOD, LREC-2014, pp. 1–6 (2014)

    Google Scholar 

  39. Richins, M.L.: Measuring emotions in the consumption experience. J. Consum. Res. 24(2), 127–146 (1997)

    Article  Google Scholar 

  40. Roberts, K., Roach, M., Johnson, J.: EmpaTweet: annotating and detecting emotions on twitter. In: Proceedings of LREC 2012, pp. 3806–3813 (2012)

    Google Scholar 

  41. SAB corpus website. http://sabcorpus.linkeddata.es

  42. Sam, K.M., Lei, P., Chatwin, C.: Ontology development for e-marketing mix model mapping with internet consumers’ decision-making styles. In: Sobh, T. (ed.) Innovations and Advanced Techniques in Computer and Information Sciences and Engineering, pp. 279–282. Springer, Dordrecht (2007). doi:10.1007/978-1-4020-6268-1_50

    Chapter  Google Scholar 

  43. Sánchez-Rada, J.F., Iglesias, C.A.: Onyx: a linked data approach to emotion representation. Inf. Process. Manag. 52(1), 99–114 (2016)

    Article  Google Scholar 

  44. Sánchez Rada, J.F., Torres, M., et al.: A linked data approach to sentiment and emotion analysis of twitter in the financial domain. In: 2nd International Workshop on Finance and Economics on the Semantic Web (2014)

    Google Scholar 

  45. Shaver, P., Schwartz, J., et al.: Emotion knowledge: further exploration of a prototype approach. J. Pers. Soc. Psychol. 52(6), 1061–1086 (1987)

    Article  Google Scholar 

  46. Spanish Corpus of reviews about films. http://www.sfu.ca/~mtaboada/research/SFU_Review_Corpus.html

  47. Strapparava, C., Valitutti, A.: WordNet-Affect: an affective extension of WordNet. In: Proceedings of LREC, pp. 1083–1086 (2004)

    Google Scholar 

  48. The Emotions & Cognition Ontology. http://rhizomik.net/html/ontologies/emotions&cognitionontology/

  49. Thomson Reuter’s PermID website. https://permid.org/

  50. Togias, K., Kameas, A.: An ontology-based representation of the twitter REST API. In: Proceedings of the IEEE 24th ICTAI, vol. 1, pp. 998–1003 (2012)

    Google Scholar 

  51. Tripadvisor website, with opinions on tourism. https://www.tripadvisor.es/

  52. Twitter preprocessed datasets available at the TU Eindhoven. http://www.win.tue.nl/~mpechen/projects/smm/#Datasets

  53. TwO, the Twitter Ontology. https://github.com/joshhanna/Twitter-Ontology

  54. Vocabulary of the SAB corpus. http://sabcorpus.linkeddata.es/vocab

  55. Westerski, A., Iglesias, C.A., Rico, F.T.: Linked opinions: describing sentiments on the structured web of data. In: Proceedings of the 4th International Workshop Social Data on the Web, vol. 830 (2011)

    Google Scholar 

  56. Yan, J., Bracewell, D.B., et al.: The creation of a Chinese emotion ontology based on HowNet. Eng. Lett. 16(1), 166–171 (2008)

    Google Scholar 

Download references

Acknowledgments

This work has been partially supported by LPS-BIGGER (IDI-20141259, Ministerio de Economía y Competitividad), a research assistant grant by the Consejería de Educación, Juventud y Deporte de la Comunidad de Madrid partially founded by the European Social Fund (PEJ16/TIC/AI-1984) and a Juan de la Cierva contract.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to María Navas-Loro .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Navas-Loro, M., Rodríguez-Doncel, V., Santana-Perez, I., Sánchez, A. (2017). Spanish Corpus for Sentiment Analysis Towards Brands. In: Karpov, A., Potapova, R., Mporas, I. (eds) Speech and Computer. SPECOM 2017. Lecture Notes in Computer Science(), vol 10458. Springer, Cham. https://doi.org/10.1007/978-3-319-66429-3_68

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-66429-3_68

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66428-6

  • Online ISBN: 978-3-319-66429-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics