As social media is increasing and consumption of data is increasing there is a big increase in spam SMS. As an example, the people using SMS- capable are almost 6.1 billion another great example is the currently famous WhatsApp which has reached 1 billion users. Increase in such social activities has also given rise to more and more illegal activities. The current activities of SMS is carried mostly in Asia. About 20-30% of SMS traffic from China and India. That’s why this spam is an emerging problem in Asia.
This growth is an open invitation for malicious organizations and more illegal activities are being carried out though this devices, many organization are arrested by the copes in many country for doing spam on people using attractive SMS with big offers and gifts, what we have done in this project is developed a model for filtering spam or ham SMS using sentiment analysis and personality recognition techniques.
Spam is an irrelevant message also used for advertisement and marketing, spreading malware. The message must be filtered out so that such messages won’t disturb the privacy of the user. The main aim of the project is to sort the message using personality recognition and sentimental analysis combined.
As previously only on the bases of sentiments the spams were filtered. Spam can be described as uninvited electronic messages sent in bulk to a group of receivers. The messages are characterized as electronic, unsolicited, commercial, mass constitutes a growing threat mainly due to the following factors: 1) the availability of low-cost bulk SMS plans; 2) reliability; 3) low chance of receiving responses from some unsuspecting receivers; and 4) the message can be personalized. Mobile SMS spam detection and prevention is an important matter. It has taken on a lot of issues and solutions inherited from relatively older scenarios of email spam detection and filtering.
The main objective of this project is to analyse these-techniques in short instant message spam filtering and also the polarity and personality dimension can improve the results obtained previously. In these project, we focus on SMS messages, which are structurally similar to other instant short messages.