Abstract
Human emotion analysis has continuously stimulated studies in different disciplines and it is spurring interest among the computer scientists too. Particularly, the growing popularity of Micro-blogging platforms, has generated large amounts of data, which in turn represent an attractive source to study social media users, especially in user-generated content analysis, such as opinion mining and sentiment analysis. In this paper, we propose to analyze micro-blogging content in order to characterize the users individually when writing posts with emotional content. The analysis is two-fold and considers the emotional content at different granularity levels, one refers to the textual units and allows us to capture the emotional state expressed by the user, the other one refers to the collections of textual units and allows us to summarize the lexicon used by the user. In particular, in the first case, we focus on a sentence-based emotion detection problem, aimed at classifying the textual units into a set of pre-defined emotion categories. The second analysis is performed through a keyword extraction approach, aimed at finding representative generic word sets in the form of prototypes of textual unit clusters. Extensive experiments conducted under different perspectives, yet always centered around the user, reveal interesting findings in terms of classification accuracy, clustering incoherence versus classifications perspectives and valuable efforts in user emotion profiling.
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Notes
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Facebook posts were collected through 2008–2018.
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We do not report their names due to privacy reasons.
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Skenduli, M.P., Biba, M. (2020). Classification and Clustering of Emotive Microblogs in Albanian: Two User-Oriented Tasks. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) Complex Pattern Mining. Studies in Computational Intelligence, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-030-36617-9_10
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