Browsing by Subject "Information and Communication Technologies"

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    Review spam detection based on psychological and linguistic features; [Psikolojik ve Dilbilimsel Özniteliklere Dayali Istenmeyen Inceleme Metni Belirleme]
    (Institute of Electrical and Electronics Engineers Inc., 2018) Onan A.
    With the advances in information and communication technologies, the immense quantity of review texts have become available on the Web. Review text can serve as an essential source of information for individual decision makers and business organizations. Some of the reviews shared on the Web may contain deceptive information to mislead the existing decision making process. In this study, we have presented a supervised learning based scheme for review spam detection. In the presented study, psychological and linguistic feature sets and their combinations are taken into consideration. In the study, the predictive performances of four conventional supervised learning methods (namely, Naive Bayes classifier, K-nearest neighbor algorithm, support vector machines and C4.5 algorithm) are evaluated on the different feature sets. © 2018 IEEE.
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    Satire Detection in Turkish News Articles: A Machine Learning Approach
    (Springer, 2019) Toçoğlu M.A.; Onan A.
    With the advances in information and communication technologies, an immense amount of information has been shared on social media and microblogging platforms. Much of the online content contains elements of figurative language, such as, irony, sarcasm and satire. The automatic identification of figurative language can be viewed as a challenging task in natural language processing, where linguistic entities, such as, metaphor, analogy, ambiguity, irony, sarcasm, satire, and so on, have been utilized to express more complex meanings. The predictive performance of sentiment classification schemes may degrade if figurative language within the text has not been properly addressed. Satirical text is a way of figurative communication, where ideas/opinions regarding a people, event or issue is expressed in a humorous way to criticize that entity. Satirical news can be deceptive and harmful. In this paper, we present a machine learning based approach to satire detection in Turkish news articles. In the presented scheme, we utilized three kinds of features to model lexical information, namely, unigrams, bigrams and tri-grams. In addition, term-frequency, term-presence and TF-IDF based schemes have been taken into consideration. In the classification phase, Naïve Bayes, support vector machines, logistic regression and C4.5 algorithms have been examined. © 2019, Springer Nature Switzerland AG.