2026-04-21T12:08:00Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/11812024-03-04T13:56:41Zcom_20.500.11984_473col_20.500.11984_478

00925njm 22002777a 4500 dc Ezpeleta, Enaitz author Garitano, Iñaki author Zurutuza, Urko author 2017 Currently, short communication channels are growing up due to the huge increase in the number of smartphones and online social networks users. This growth attracts malicious campaigns, such as spam campaigns, that are a direct threat to the security and privacy of the users. While most researches are focused on automatic text classification, in this work we demonstrate the possibility of improving current short messages spam detection systems using a novel method. We combine personality recognition and sentiment analysis techniques to analyze Short Message Services (SMS) texts. We enrich a publicly available dataset adding these features, first separately and after in combination, of each message to the dataset, creating new datasets. We apply several combinations of the best SMS spam classifiers and filters to each dataset in order to compare the results of each one. Taking into account the experimental results we analyze the real inuence of each feature and the combination of both. At the end, the best results are improved in terms of accuracy, reaching to a 99.01% and the number of false positive is reduced. 0218-4885 Print 1793-6411 Online https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=128257 https://hdl.handle.net/20.500.11984/1181 spam polarity SMS sentiment analysis security Short Messages Spam Filtering Combining Personality Recognition and Sentiment Analysis