Abstract
The aim of this paper is to analyze the influence of sentiment-related terms on the automatic detection of topics in social networks. The study is based on the use of an ontology, to which the capacity to gradually identify and discard sentiment terms in social network texts is incorporated, as these terms do not provide useful information for detecting topics. To detect these terms, we have used two resources focused on the analysis of sentiments. The proposed system has been assessed with real data sets of the social networks Twitter and Dreamcatcher in English and Spanish respectively.
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Acknowledgements
This research was partially supported by the Andalusian Government (Junta de Andalucía) under projects P11-TIC-7460 and P10-TIC-6109.
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Gutiérrez-Batista, K., Campaña, J.R., Vila, MA., Martin-Bautista, M.J. (2018). Fuzzy Analysis of Sentiment Terms for Topic Detection Process in Social Networks. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-319-91476-3_1
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