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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.

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This paper presents an SMS based design in the GSM system for a portable, light weighted, and small sized TeleAlarm device. The device is composed of a transmitter and a controller. When an emergent situation such as a stroke or a fall occurs, the user only needs to push a button to trigger the controller. The controller automatically sends text messages stored in its database through the transmitter to specified mobile phone numbers for help. The SMS uses only the control channels in the GSM system to transfer the message, which enables the receiving-end user to receive it even during a call. An experimental test shows that a complete message transmission only needs 2.949 seconds in average. The design is convenient to elderly people who may live alone.