Abstract
This paper focuses on analyzing the underlying sentiment of news articles, taken to be factual rather than comprised of opinions. The sentiment of each article towards a specific theme can be expressed in fuzzy linguistic terms and aggregated into a centralized sentiment which can be trended. This allows the interpretation of sentiments without conversion to numerical values. The methodology, as defined, maintains the range of sentiment articulated in each news article. In addition, a measure of consensus is defined for each day as the degree to which the articles published agree in terms of the sentiment presented. A real case example is presented for a controversial event in recent history with the analysis of 82,054 articles over a three day period. The results show that considering linguistic terms obtain compatible values to numerical values, however in a more humanistic expression. In addition, the methodology returns an internal consensus among all the articles written each day for a specific country. Therefore, hesitant linguistic terms can be considered well suited for expressing the tone of articles.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme (grant agreement n\(^\circ \) 822654).
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References
Cain-Arzu, D.L.: Sensationalism in newspapers: a look at the reporter Andamandala in Belize 2010–2014 (2016)
Cao, N., Lu, L., Lin, Y.R., Wang, F., Wen, Z.: SocialHelix: visual analysis of sentiment divergence in social media. J. vis. 18(2), 221–235 (2015)
Corea, F.: Can Twitter proxy the investors’ sentiment? The case for the technology sector. Big Data Res. 4, 70–74 (2016)
Ducat, L., Thomas, S., Blood, W.: Sensationalising sex offenders and sexual recidivism: impact of the serious sex offender monitoring act 2005 on media reportage. Aust. Psychol. 44(3), 156–165 (2009)
Galla, D., Burke, J.: Predicting social unrest using GDELT. In: Perner, P. (ed.) MLDM 2018. LNCS (LNAI), vol. 10935, pp. 103–116. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96133-0_8
Giachanou, A., Crestani, F.: Tracking sentiment by time series analysis. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 1037–1040 (2016)
Hendriks Vettehen, P., Nuijten, K., Beentjes, J.: News in an age of competition: the case of sensationalism in Dutch television news, 1995–2001. J. Broadcast. Electron. Media 49(3), 282–295 (2005)
Kolagani, S.H.D., Negahban, A., Witt, C.: Identifying trending sentiments in the 2016 us presidential election: a case study of Twitter analytics. Issues Inf. Syst. 18(2), 80–86 (2017)
Kwak, H., An, J.: A first look at global news coverage of disasters by using the GDELT dataset. In: Aiello, L.M., McFarland, D. (eds.) SocInfo 2014. LNCS, vol. 8851, pp. 300–308. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13734-6_22
Leetaru, K., Schrodt, P.A.: GDELT: global data on events, location, and tone, 1979–2012. In: ISA Annual Convention, vol. 2, pp. 1–49. Citeseer (2013)
McCombs, M.E., Shaw, D.L.: The agenda-setting function of mass media. Public Opin. Q. 36(2), 176–187 (1972)
Montserrat-Adell, J., Agell, N., Sánchez, M., Ruiz, F.J.: A representative in group decision by means of the extended set of hesitant fuzzy linguistic term sets. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Yañez, C. (eds.) Modeling Decisions for Artificial Intelligence, pp. 56–67. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45656-0_5
Montserrat-Adell, J., Agell, N., Sánchez, M., Prats, F., Ruiz, F.J.: Modeling group assessments by means of hesitant fuzzy linguistic term sets. J. Appl. Logic 23, 40–50 (2017)
Montserrat-Adell, J., Agell, N., Sánchez, M., Ruiz, F.J.: Consensus, dissension and precision in group decision making by means of an algebraic extension of hesitant fuzzy linguistic term sets. Inf. Fusion 42, 1–11 (2018)
Mutz, D.C., Soss, J.: Reading public opinion: the influence of news coverage on perceptions of public sentiment. Public Opin. Q. 61, 431–451 (1997)
Prabowo, R., Thelwall, M.: Sentiment analysis: a combined approach. J. Inf. 3(2), 143–157 (2009)
Rodriguez, R.M., Martinez, L., Herrera, F.: Hesitant fuzzy linguistic term sets for decision making. IEEE Trans. Fuzzy Syst. 20(1), 109–119 (2011)
Vinodhini, G., Chandrasekaran, R.: Sentiment analysis and opinion mining: a survey. Int. J. 2(6), 282–292 (2012)
Yaqub, U., Chun, S.A., Atluri, V., Vaidya, J.: Sentiment based analysis of tweets during the us presidential elections. In: Proceedings of the 18th Annual International Conference on Digital Government Research, pp. 1–10 (2017)
Yonamine, J.E.: A nuanced study of political conflict using the global datasets of events location and tone (GDELT) dataset (2013)
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Nguyen, J., Armisen, A., Agell, N., Saz, Á. (2020). Aggregating News Reporting Sentiment by Means of Hesitant Linguistic Terms. In: Torra, V., Narukawa, Y., Nin, J., Agell, N. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2020. Lecture Notes in Computer Science(), vol 12256. Springer, Cham. https://doi.org/10.1007/978-3-030-57524-3_21
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