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Interpreting Reputation Through Frequent Named Entities in Twitter

  • Nacéra Bennacer
  • Francesca BugiottiEmail author
  • Moditha Hewasinghage
  • Suela Isaj
  • Gianluca Quercini
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10569)

Abstract

Twitter is a social network that provides a powerful source of data. The analysis of those data offers many challenges among those stands out the opportunity to find the reputation of a product, of a person, or of any other entity of interest. Several tools for sentiment analysis have been built in order to calculate the general opinion of an entity using a static analysis of the sentiments expressed in tweets. However, entities are not static; they collaborate with other entities and get involved in events. A simple aggregation of sentiments is then not sufficient to represent this dynamism. In this paper, we present a new approach that identifies the reputation of an entity on the basis of the set of events it is involved into by providing a transparent and self explanatory way for interpreting reputation. In order to perform this analysis we define a new sampling method based on a tweet weighting to retrieve relevant information. In our experiments we show that the 90% of the reputation of the entity originates from the events it is involved into, especially in the case of entities that represent public figures.

Keywords

Reputation Frequent itemsets Sampling Opinion mining 
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References

  1. 1.
    Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of Twitter data . In: Proceedings of the Workshop on Languages in Social Media, Association for Computational Linguistics, pp. 30–38 (2011)Google Scholar
  2. 2.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. Acm Sigmod Rec. 22, 207–216 (1993)CrossRefGoogle Scholar
  3. 3.
    Bizhanova, A., Uchida, O.: Product reputation trend extraction from Twitter. Soc. Netw., Scientific Research Publishing (2014)Google Scholar
  4. 4.
    Bollen, J., Mao, H., Pepe, A.: Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. ICWSM Conf. 11, 450–453 (2011)Google Scholar
  5. 5.
    Ding, X., Liu, B., Zhang, L.: Entity discovery and assignment for opinion mining applications. In: KDD Conference, pp. 1125–1134 (2009)Google Scholar
  6. 6.
    Gabielkov, M., Rao, A., Legout, A.: Sampling online social networks: an experimental study of Twitter. In: ACM SIGCOMM Conference, pp. 127–128 (2014)Google Scholar
  7. 7.
    Ghosh, S., Zafar, M.B., Bhattacharya, P., Sharma, N., Ganguly, N., Gummadi, K.: On sampling the wisdom of crowds: random vs. expert sampling of the Twitter stream. In: CKIM Conference, pp. 1739–1744 (2013)Google Scholar
  8. 8.
    Gjoka, M., Kurant, M., Butts, C.T., Markopoulou, A.: Walking in facebook: a case study of unbiased sampling of OSNs. In: Infocom, pp. 1–9 (2010)Google Scholar
  9. 9.
    Gouriten, G., Maniu, S., Senellart, P.: Scalable, generic, and adaptive systems for focused crawling. In: HT ACM Conference, pp. 35–45 (2014)Google Scholar
  10. 10.
    Hangya, V., Berend, G., Farkas, R.: SZTE-NLP: sentiment detection on Twitter messages. SEM Conf. 2, 549–553 (2013)Google Scholar
  11. 11.
    Hogenboom, A., Bal, D., Frasincar, F., Bal, M., de Jong, F., Kaymak, U.: Exploiting emoticons in sentiment analysis. In: ACM Symposium on Applied, Computing, pp. 703–710 (2013)Google Scholar
  12. 12.
    Meng, X., Wei, F., Liu, X., Zhou, M., Li, S., Wang, H.: Entity-centric topic-oriented opinion summarization in Twitter. In: KDD Conference, pp. 379–387 (2012)Google Scholar
  13. 13.
    Saif, H., He, Y., Alani, H.: Semantic sentiment analysis of Twitter. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012. LNCS, vol. 7649, pp. 508–524. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-35176-1_32CrossRefGoogle Scholar
  14. 14.
    Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment in Twitter events. ISI J. 62(2), 406–418 (2011). WileyGoogle Scholar
  15. 15.
    Van Canneyt, S., Claeys, N., Dhoedt, B.: Topic-dependent sentiment classification on Twitter. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 441–446. Springer, Cham (2015). doi: 10.1007/978-3-319-16354-3_48CrossRefGoogle Scholar
  16. 16.
    Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: Topic sentiment analysis in Twitter: a graph-based hashtag sentiment classification approach. In: CKIM Conference, pp. 1031–1040 (2011)Google Scholar
  17. 17.
    Xiang, B., Zhou, L., Reuters, T.: Improving Twitter sentiment analysis with topic-based mixture modeling and semi-supervised training. In: ACL Conference, pp. 434–439 (2014)Google Scholar
  18. 18.
    Zhou, Z., Zhang, X., Sanderson, M.: Sentiment analysis on Twitter through topic-based lexicon expansion. In: Wang, H., Sharaf, M.A. (eds.) ADC 2014. LNCS, vol. 8506, pp. 98–109. Springer, Cham (2014). doi: 10.1007/978-3-319-08608-8_9CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nacéra Bennacer
    • 1
  • Francesca Bugiotti
    • 1
    Email author
  • Moditha Hewasinghage
    • 1
  • Suela Isaj
    • 1
  • Gianluca Quercini
    • 1
  1. 1.LRI, CentraleSupélecParis-Saclay UniversityGif-sur-YvetteFrance

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