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.
This is a preview of subscription content, log in via an institution to check access.
References
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)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. Acm Sigmod Rec. 22, 207–216 (1993)
Bizhanova, A., Uchida, O.: Product reputation trend extraction from Twitter. Soc. Netw., Scientific Research Publishing (2014)
Bollen, J., Mao, H., Pepe, A.: Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. ICWSM Conf. 11, 450–453 (2011)
Ding, X., Liu, B., Zhang, L.: Entity discovery and assignment for opinion mining applications. In: KDD Conference, pp. 1125–1134 (2009)
Gabielkov, M., Rao, A., Legout, A.: Sampling online social networks: an experimental study of Twitter. In: ACM SIGCOMM Conference, pp. 127–128 (2014)
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)
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)
Gouriten, G., Maniu, S., Senellart, P.: Scalable, generic, and adaptive systems for focused crawling. In: HT ACM Conference, pp. 35–45 (2014)
Hangya, V., Berend, G., Farkas, R.: SZTE-NLP: sentiment detection on Twitter messages. SEM Conf. 2, 549–553 (2013)
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)
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)
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_32
Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment in Twitter events. ISI J. 62(2), 406–418 (2011). Wiley
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_48
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)
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)
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_9
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Bennacer, N., Bugiotti, F., Hewasinghage, M., Isaj, S., Quercini, G. (2017). Interpreting Reputation Through Frequent Named Entities in Twitter. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10569. Springer, Cham. https://doi.org/10.1007/978-3-319-68783-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-68783-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68782-7
Online ISBN: 978-3-319-68783-4
eBook Packages: Computer ScienceComputer Science (R0)