http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Wandabwa (검색결과 2 건)
- 무료
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Twitter Crossfire : Terror Attack Detection via Probabilistic Classifiers
Herman Wandabwa,Liao Zhifang,Korir Sammy 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.4
The advent of social computing brought with it different social networking platforms. The idea of surfers socializing with people of different backgrounds as well as geographical regions is quite fascinating. In our approach, we delved deeper in disaster discovery whereby we extracted panic related attributes and trained them with real data in three disaster scenarios in different parts of the world. Fine tuning of the final attributes led to accuracies above 91% proving the fact that with proper attribute selection and handling of sparse data balance, it’s possible to detect related disasters as soon as related tweets appear. We believe that we are the first to use probabilistic classifiers approach as well as NLP in specifically human induced terror attacks detection as there is no known system currently that solely caters for these.
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Detecting Polarizing Language in Twitter using Topic Models and ML Algorithms
Njagi Dennis Gitari,Zhang Zuping,Wandabwa Herman 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.9
The upsurge in the use of social media in public discourses has made it possible for social scientists to engage in emerging and interesting areas of research. Normally, public debates tend to assume polar positions along political, social or ideological lines. Generally, polarity in the language used is more of blaming the opposing group in such debates. In this paper, we investigated the detection of polarizing language in tweets in the event of a disaster. Our approach entails combining topic modeling and Machine Learning (ML) algorithms to generate topics that we consider to be polarized thereby classifying a given tweet as polar or not. Our latent Dirichlet allocation (LDA)-based model incorporates external resources in the form of a lexicon of blame-oriented words to induce the generation of polar topics. The Collapsed Gibbs sampling is used to infer new documents and to estimate the values of parameters employed in our model. We computed the log likelihood (LL) ratios using our model and two other state-of-the-art LDA-based models for evaluation. Furthermore, we compared polarized detection classification accuracy using the features extracted from polarized topics, bag of words (BOW) and part of speech (POS)-based features. Preliminary experiments returned higher overall accuracy results of 87.67% using topic-based features compared to BOW and POS-based features.
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