@article{MAKHILLJEAS201712314151,
    title = {Terrorism Detection Based on Sentiment Analysis Using Machine Learning},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {3},
    pages = {691-698},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.691.698},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.691.698},
    author = {Sofea Azrina and},
    keywords = {Sentiment,naive bayes,statement,sentiment,accurately},
    abstract = {The advancement in technology especially a micro-blogging site such as Twitter has brought a new
era in terrorism where social media is being used as a platform of communication, incite the act of terrorism,
recruitment and much more. Terrorist and people supporting this group tend to include sentiment leads to
terrorism when sharing their opinions and comments. Thus, sentiment analytics can help to explore and classify
the opinion from users to different polarity. Sentiment analysis is an opinion mining process from computer
linguistics perspective. There are many existing techniques that have been improved to determine user’s
opinions in social media but most of the current techniques and algorithms are not explicit to sense the acts of
terrorism. Thus, this research is one of the approach to sense user’s act leading to terrorism based on the tweets
they shared at the Twitter platform. A comparative study between sentiment analysis techniques has been
conducted and analysed. In this report, it is proposed to improvise the current sentiment analysis techniques
by using machine learning to detect the acts of terrorism more accurately. The novelty about this research is
after the sentence have being categorized into positive, negative and neutral categories, all these categories
will be compared against the previous sentence of a particular account holder based on the sentiment score for
the latest and previous sentence. This means, the tweet’s history of a particular account holder on each
category will be extracted and the sentiment score calculated. Then, the sentiment score of previous statement
will be compared with the sentiment score of the latest statement detected. Machine learning is being proposed
to be used in this research as it is more accurate as compared to lexicon-based approach.}
    }