(untitled) (International Journal of Engineering and Computer Science, 2017)
Twitter is prone to malicious tweets containing URLs for spam, phishing, and malware distribution. Conventional Twitter spam detection schemes utilize account features such as the ratio of tweets containing URLs and the account creation date, or relation features in the Twitter graph. The techniques misses’ one major factor in the assessment and that is the content of the tweets. So basically we have developed software which on the basis of content of the tweet classifies using naïve bayes classifier whether the tweet is positive or negative. Also we have gone a level up with this software and that is, not only profile specific assessment of tweets can be done but also a person can search for words and look into the tweets that contain the word twitter wide to get individual assessment of the tweets as well as overall statistical data in form of a pie chart. Content specific assessment is important because one can have a verified twitter account with all the technical parameters satisfying and can still spread malicious content or the account could be hacked in to spread malicious content. For the solution, we have come up with the software.