Skip to main content
SpringerLink
Log in
Book cover

International Conference on Technologies and Innovation

CITI 2016: Technologies and Innovation pp 63–76Cite as

Sentiment Analysis and Trend Detection in Twitter

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 658))

Abstract

Social networks such as Twitter are considered a rich resource of information about actual world actions of all types. Several efforts have been dedicated to trend detection on Twitter i.e., the current popular topics of conversation among its users. However, despite these efforts, sentiment analysis is not taken into account. Sentiment analysis is the field of study that analyzes people’s opinions and moods. Therefore, applying sentiment analysis to tweets related to a trending topic also enables to know if people are talking positively or negatively about it, thus providing important information for real-time decision making in various domains. On the basis of this understanding, we propose SentiTrend, a system for trend detection on twitter and its corresponding sentiment analysis. In this paper, we present the SentiTrend’s architecture and functionality. Also, the evaluation results concerning the effectiveness of our approach to trend detection and sentiment analysis are presented. Our proposal obtained encouraging results with an average F-measure of 80.7 % for sentiment classification, and an average F-measure 80.0 % and 75.5 % for trend detection.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

References

  1. Martinez-Romo, J., Araujo, L.: Detecting malicious tweets in trending topics using a statistical analysis of language. Expert Syst. Appl. 40(8), 2992–3000 (2013)

    Article  Google Scholar 

  2. Atefeh, F., Khreich, W.: A survey of techniques for event detection in Twitter. Comput Intell. 31(1), 132–164 (2015)

    Article  MathSciNet  Google Scholar 

  3. Kontopoulos, E., Berberidis, C., Dergiades, T., Bassiliades, N.: Ontology-based sentiment analysis of Twitter posts. Expert Syst. Appl. 40(10), 4065–4074 (2013)

    Article  Google Scholar 

  4. González-Ibáñez, R., Muresan, S., Wacholder, N.: Identifying sarcasm in Twitter: a closer look. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 2, pp. 581–586, Stroudsburg, PA, USA (2011)

    Google Scholar 

  5. Paltoglou, G., Thelwall, M.: Twitter, MySpace, Digg: unsupervised sentiment analysis in social media. ACM Trans Intell Syst Technol. 3(4), 66 (2012)

    Article  Google Scholar 

  6. Kaleel, S.B., Abhari, A.: Cluster-discovery of Twitter messages for event detection and trending. J. Comput. Sci. 6, 47–57 (2015)

    Article  Google Scholar 

  7. Benhardus, J., Kalita, J.: Streaming trend detection in Twitter. Int. J. Web Based Communities 9(1), 122–139 (2013)

    Article  Google Scholar 

  8. MacCartney, B.: Stanford Classifer, The Stanford Natural Language Processing Group (2015). http://nlp.stanford.edu/software/classifier.shtml. Accessed 18 May 2015

  9. Ghosh, M., Animesh, K.: Unsupervised linguistic approach for sentiment classification from online reviews using SentiWordNet 3.0. Int. J. Eng. Res. Technol. 2(9), 55–60 (2013)

    Google Scholar 

  10. Peñalver-Martinez, I., Garcia-Sanchez, F., Valencia-Garcia, R., Rodríguez-García, M.A., Moreno, V., Fraga, A., Sánchez-Cervantes, J.L.: Feature-based opinion mining through ontologies. Expert Syst. Appl. 41(13), 5995–6008 (2014)

    Article  Google Scholar 

  11. Montejo-Ráez, A., Martínez-Cámara, E., Martín-Valdivia, M.T., Ureña-López, L.A.: A knowledge-based approach for polarity classification in Twitter. J. Assoc. Inf. Sci. Technol. 65(2), 414–425 (2014)

    Article  Google Scholar 

  12. Ye, Q., Zhang, Z., Law, R.: Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst. Appl. 36(3), 6527–6535 (2009)

    Article  Google Scholar 

  13. Mohammad, S.M., Zhu, X., Kiritchenko, S., Martin, J.: Sentiment, emotion, purpose, and style in electoral tweets. Inf. Process. Manag. 51(4), 480–499 (2015)

    Article  Google Scholar 

  14. Sidorov, G., et al.: Empirical study of machine learning based approach for opinion mining in tweets. In: Batyrshin, I., González Mendoza, M. (eds.) MICAI 2012, Part I. LNCS, vol. 7629, pp. 1–14. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  15. Salas-Zárate, M.P., Paredes-Valverde, M.A., Limon-Romero, J., Tlapa, D., Baez-Lopez, Y.: Sentiment classification of Spanish reviews: an approach based on feature selection and machine learning methods. J. UCS 22(5), 691–708 (2016)

    Google Scholar 

  16. Habernal, I., Ptáček, T., Steinberger, J.: Supervised sentiment analysis in Czech social media. Inf. Process. Manag. 50(5), 693–707 (2014)

    Article  Google Scholar 

  17. Balage Filho, P.P., Pardo, T.A., Alusio, S.M.: An evaluation of the Brazilian Portuguese LIWC dictionary for sentiment analysis. In: Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology, Fortaleza, Ceara, pp. 215–219 (2013)

    Google Scholar 

  18. Salas-Zárate, M.P., López-López, E., Valencia-García, R., Aussenac-Gilles, N., Almela, Á., Alor-Hernández, G.: A study on LIWC categories for opinion mining in Spanish reviews. J. Inf. Sci. 40(6), 749–760 (2014)

    Article  Google Scholar 

  19. Lu, R., Yang, Q.: Trend analysis of news topics on Twitter. Int. J. Mach. Learn. Comput. 2(3), 327 (2012)

    Article  Google Scholar 

  20. Ding, L., Pang, C., Kew, L.M., Jain, L.C., Howlett, R.J., Weilin, L., Hoon, G.K.: Personalization of trending tweets using like-dislike category model. Procedia Comput. Sci. 60, 236–245 (2015)

    Article  Google Scholar 

  21. Mathioudakis, M., Koudas, N.: TwitterMonitor: trend detection over the TwitterStream. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, pp. 1155–1158. ACM, New York (2010)

    Google Scholar 

  22. Paredes-Valverde, M.A., Valencia-García, R., Rodríguez-García, M.A., Colomo-Palacios, R., Alor-Hernández, G.: A semantic-based approach for querying linked data using natural language. J. Inf. Sci. (2015) doi:10.1177/0165551515616311

    Google Scholar 

  23. Paredes-Valverde, M.A., Rodríguez-García, M.Á., Ruiz-Martínez, A., Valencia-García, R., Alor-Hernández, G.: ONLI: an ontology-based system for querying DBpedia using natural language paradigm. Expert Syst. Appl. 42(12), 5163–5176 (2015)

    Article  Google Scholar 

  24. Agarwal, B., Mittal, N.: Prominent feature extraction for review analysis: an empirical study. J. Exp. Theoret. Artif. Intell. 28(3), 485–498 (2016)

    Article  Google Scholar 

  25. Elshater, Y., Elgazzar, K., Martin, P.: goDiscovery: web service discovery made efficient. In: 2015 IEEE International Conference on Web Services (ICWS), pp. 711–716 (2015)

    Google Scholar 

  26. Rushdi Saleh, M., Martín-Valdivia, M.T., Montejo-Ráez, A., Ureña-López, L.A.: Experiments with SVM to classify opinions in different domains. Expert Syst. Appl. 38(12), 14799–14804 (2011)

    Article  Google Scholar 

  27. Moraes, R., Valiati, J.F., Gavião Neto, W.P.: Document-level sentiment classification: an empirical comparison between SVM and ANN. Expert Syst. Appl. 40(2), 621–633 (2013)

    Article  Google Scholar 

  28. Xia, R., Zong, C., Li, S.: Ensemble of feature sets and classification algorithms for sentiment classification. Inf. Sci. 181(6), 1138–1152 (2011)

    Article  Google Scholar 

  29. 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(1), 93–107 (2014)

    Article  Google Scholar 

  30. He, Y., Zhou, D.: Self-training from labeled features for sentiment analysis. Inf. Process. Manag. 47(4), 606–616 (2011)

    Article  Google Scholar 

  31. Salas-Zárate, M.P., Valencia-García, R., Ruiz-Martínez, A., Colomo-Palacios, R.: Feature-based opinion mining in financial news: Aan ontology-driven approach. J. Inf. Sci. (2016). doi:10.1177/0165551516645528

    Google Scholar 

  32. Shah, H., Bhandari, P., Mistry, K., Thakor, S., Patel, M., Ahir, K.: Study of named entity recognition for indian languages. Int. J. Inf. 6(1), 11–25 (2016)

    Google Scholar 

  33. Bouckaert, R.R., Frank, E., Hall, M.A., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: WEKA − experiences with a Java open-source project. J. Mach. Learn. Res. 11, 2533–2541 (2010)

    MATH  Google Scholar 

  34. Hofmann, M., Klinkenberg, R.: RapidMiner: Data Mining Use Cases and Business Analytics Applications. CRC Press, Boca Raton (2013)

    Google Scholar 

  35. Antonakaki, D., Polakis, I., Athanasopoulos, E., Ioannidis, S., Fragopoulou, P.: Exploiting abused trending topics to identify spam campaigns in Twitter. Soc. Netw. Anal. Min. 6(1), 1–11 (2016)

    Article  Google Scholar 

Download references

Acknowledgments

María del Pilar Salas-Zárate and Mario Andrés Paredes-Valverde are supported by the National Council of Science and Technology (CONACYT), the Public Education Secretary (SEP) and the Mexican government.

Author information

Authors and Affiliations

  1. Departamento de Informática y Sistemas, Universidad de Murcia, 30100, Murcia, Spain

    María del Pilar Salas-Zárate, Mario Andrés Paredes-Valverde & Rafael Valencia-García

  2. Universidad Agraria del Ecuador, Avenida 25 de Julio, Guayaquil, Ecuador

    José Medina-Moreira & Katty Lagos-Ortiz

  3. Universidad de Guayaquil Cdla. Universitaria Salvador Allende, Guayaquil, Ecuador

    José Medina-Moreira, Paul Javier Álvarez-Sagubay & Katty Lagos-Ortiz

Authors
  1. María del Pilar Salas-Zárate
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José Medina-Moreira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paul Javier Álvarez-Sagubay
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Katty Lagos-Ortiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mario Andrés Paredes-Valverde
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rafael Valencia-García
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to María del Pilar Salas-Zárate .

Editor information

Editors and Affiliations

  1. Universidad de Murcia, Murcia, Spain

    Rafael Valencia-García

  2. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Katty Lagos-Ortiz

  3. Universidad de Castilla-La Mancha, Toledo, Spain

    Gema Alcaraz-Mármol

  4. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Javier del Cioppo

  5. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Nestor Vera-Lucio

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Salas-Zárate, M.d.P., Medina-Moreira, J., Álvarez-Sagubay, P.J., Lagos-Ortiz, K., Paredes-Valverde, M.A., Valencia-García, R. (2016). Sentiment Analysis and Trend Detection in Twitter. In: Valencia-García, R., Lagos-Ortiz, K., Alcaraz-Mármol, G., del Cioppo, J., Vera-Lucio, N. (eds) Technologies and Innovation. CITI 2016. Communications in Computer and Information Science, vol 658. Springer, Cham. https://doi.org/10.1007/978-3-319-48024-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48024-4_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48023-7

  • Online ISBN: 978-3-319-48024-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation