TY  - JOUR
T1  - Terrorist Affiliations Identifying Through Twitter Social Media Analysis Using
Data Mining and Web Mapping Techniques
AU - Elah Al-Khalisy, Muhanad Abdul AU - B. Jehlol, Hashem 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 17
SP  - 7459
EP  - 7464
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.7459.7464
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.7459.7464
KW  - streaming API
KW  -terrarium
KW  -machine learning
KW  -social media
KW  -sentiment analysis
KW  -Text mining
KW  -GeoJSON
KW  -Naive Bayes
AB  - With the increase in number of users on each day on a social media platform that generates a huge amount of data today data analysis plays a vital role. We focus on Twitter’s mining role in extracting useful information that provides terrarium supporter data such as location, account name and terrarium propaganda. The proposed methods utilize Twitter streaming API to collect data, preprocessing and cleansing were performed on Tweet’s data, wordlist of synonyms and antonyms words relating to terrorism get it from the dictionary, these words classified as positive and negative words. The proposed methods base on “Bag-of-Word” characteristic extraction to compute the total score of each Tweet that represents training data. Depending on the training data, the Naive Bayes classifiers classify each Tweet to positive, negative and natural. GeoJSON used to find and visualize where terrarium is located online. The results can be used by the governments and security agencies to determine relevant data to find terrarium users.
ER  -