MAS: A Corpus of Tweets for Marketing in Spanish

  • María Navas-LoroEmail author
  • Víctor Rodríguez-Doncel
  • Idafen Santana-Pérez
  • Alba Fernández-Izquierdo
  • Alberto Sánchez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11155)


This paper presents a corpus of tweets in Spanish language which were manually tagged for marketing purposes. The used tags describe three aspects of the text of each Twitter post. First, the emotions a brand caused to the author from among a taxonomy of emotions designed by marketing experts. Also, whether it mentioned any element of the marketing mix (including various relevant marketing concepts such as price or promotion). Finally, the position of the author of the tweet with respect to the acquisition process (or purchase funnel). Each Twitter post is related to only one brand, which is also indicated in the corpus. The corpus presented in this article is published in a machine-readable format as a collection of RDF documents with links to additional external information. The paper also includes details on the used vocabulary and the tagging criteria, as well as a description of the annotation process followed to tag the tweets.


Corpus Marketing Marketing mix Sentiment analysis NLP Purchase funnel Emotion analysis 



This work has been partially supported by LPS-BIGGER (IDI-20141259), esTextAnalytics project (RTC-2016-4952-7), a Predoctoral grant by the Consejo de Educación, Juventud y Deporte de la Comunidad de Madrid partially founded by the European Social Fund, two Predoctoral grants from the I+D+i program of the Universidad Politécnica de Madrid and a Juan de la Cierva contract. We would also want to thank Pablo Calleja for his help in corpora statistics extraction.


  1. 1.
    Aguado, G., et al.: Análisis de sentimientos de un corpus de redes sociales. In: Actas del 31er Congreso Asociación Española de Lingüstica Aplicada. Comunicación, Cognición y Cibernética, pp. 522–534 (2013).
  2. 2.
    Bel, N., Diz-pico, J., Pocostales, J.: Classifying short texts for a Social Media monitoring system. Clasificación de textos cortos para un sistema monitor de los Social Media. Procesamiento del Lenguaje Nat. 59, 57–64 (2017)Google Scholar
  3. 3.
    Borden, N.H.: The concept of the marketing mix. J. Advertising Res. 4(2), 2–7 (1964)Google Scholar
  4. 4.
    Bruyn, A.D., Lilien, G.L.: A multi-stage model of word-of-mouth influence through viral marketing. Int. J. Res. Mark. 25(3), 151–163 (2008)CrossRefGoogle Scholar
  5. 5.
    Cohan-Sujay, C., Madhulika, Y.: Intention analysis for sales, marketing and customer service. In: Proceedings of COLING 2012, Demonstration Papers, pp. 33–40, December 2012Google Scholar
  6. 6.
    Cumbreras, M.Á.G., Cámara, E.M., et al.: TASS 2015 - The evolution of the Spanish opinion mining systems. Procesamiento de Lenguaje Nat. 56, 33–40 (2016)Google Scholar
  7. 7.
    Elzinga, D., Mulder, S., Vetvik, O.J., et al.: The consumer decision journey. McKinsey Q. 3, 96–107 (2009)Google Scholar
  8. 8.
    García-Silva, A., Rodríguez-Doncel, V., Corcho, Ó.: Semantic characterization of tweets using topic models: a use case in the entertainment domain. Int. J. Semantic Web Inf. Syst. 9(3), 1–13 (2013)CrossRefGoogle Scholar
  9. 9.
    Goldberg, A.B., Fillmore, N., Andrzejewski, D., Xu, Z., Gibson, B., Zhu, X.: May all your wishes come true : a study of wishes and how to recognize them. In: Proceedings of Human Language Technologies: NAACL 2009 (June), pp. 263–271 (2009)Google Scholar
  10. 10.
    Hasan, M., Kotov, A., Mohan, A., Lu, S., Stieg, P.M.: Feedback or research: separating pre-purchase from post-purchase consumer reviews. In: Ferro, N., et al. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 682–688. Springer, Cham (2016). Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    McCarthy, E.: Basic Marketing, A Managerial Approach, 6th edn. Richard D. Irwin, Inc., Homewood (1978)Google Scholar
  13. 13.
    Moghaddam, S.: Beyond sentiment analysis: mining defects and improvements from customer feedback. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 400–410. Springer, Cham (2015). Scholar
  14. 14.
    Mohamed, H., Mohamed, S.G., Lamjed, B.S.: Customer intentions analysis of twitter based on semantic patterns, pp. 2–6 (2015)Google Scholar
  15. 15.
    Molina-González, M.D., Martínez-Cámara, E., et al.: Cross-domain sentiment analysis using Spanish opinionated words. In: Proceedings of NLDB, pp. 214–219 (2014)Google Scholar
  16. 16.
    Navas-Loro, M., Rodríguez-Doncel, V., Santana-Perez, I., Sánchez, A.: Spanish corpus for sentiment analysis towards brands. In: Proceedings of the 19th International Conference on Speech and Computer (SPECOM), pp. 680–689 (2017)Google Scholar
  17. 17.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREc, vol. 10 (2010)Google Scholar
  18. 18.
    Plaza-Del-Arco, F.M., Martín-Valdivia, M.T., et al.: COPOS: corpus of patient opinions in Spanish. Application of sentiment analysis techniques. Procesamiento de Lenguaje Nat. 57, 83–90 (2016)Google Scholar
  19. 19.
    Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: Semeval-2014 task 4: aspect based sentiment analysis, pp. 27–35, January 2014Google Scholar
  20. 20.
    Pontiki, M., et al.: Semeval-2016 task 5: aspect based sentiment analysis. In: Proceedings of SemEval-2016, pp. 19–30. ACL, San Diego, June 2016Google Scholar
  21. 21.
    Pontiki, M., et al.: SemEval-2016 task 5: aspect based sentiment analysis. In: ProWorkshop SemEval-2016, pp. 19–30. ACL (2016)Google Scholar
  22. 22.
    Ramanand, J., Bhavsar, K., Pedanekar, N.: Wishful thinking: finding suggestions and ‘buy’ wishes from product reviews. In: Proceedings of the NAACL HLT 2010 Workshop (CAAGET 2010) (June), pp. 54–61 (2010)Google Scholar
  23. 23.
    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
  24. 24.
    Sánchez Rada, J.F., Torres, M., et al.: A linked data approach to sentiment and emotion analysis of twitter in the financial domain. In: FEOSW (2014)Google Scholar
  25. 25.
    Van Waterschoot, W., Van den Bulte, C.: The 4P classification of the marketing mix revisited. J. Mark. 56, 83–93 (1992)Google Scholar
  26. 26.
    Vázquez, S., Muñoz-García, O., Campanella, I., Poch, M., Fisas, B., Bel, N., Andreu, G.: A classification of user-generated content into consumer decision journey stages. Neural Networks 58(Suppl. C), 68–81 (2014). Special Issue on “Affective Neural Networks and Cognitive Learning Systems for Big Data Analysis”Google Scholar
  27. 27.
    Vineet, G., Devesh, V., Harsh, J., Deepam, K., Shweta, K.: Identifying purchase intent from social posts. In: ICWSM 2014, pp. 180–186 (2014)Google Scholar
  28. 28.
    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

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Ontology Engineering GroupUniversidad Politécnica de MadridMadridSpain
  2. 2.Havas MediaMadridSpain

Personalised recommendations