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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.

This paper has analyzed the situation of existent fire alarm systems and has proposed a new method of connecting local fire alarm controllers to build a much larger and more powerful system, with GSM (Global System for Mobile Communications) as the communication medium. All these local fire alarm controllers, which work as slave stations, and a central monitor, which works as a master station, are incorporated in the GSM network. Information is transmitted between the master station and slave stations through SMS (Short Message Service). The general structure of the system is presented in this paper. It is a three-hierarchy model and it consists of three subsystems: detecting and controlling subsystem, communication subsystem and central monitor subsystem. The function of each subsystem is explained and the hardware and software details of the communication subsystem are described.