Satire Detection in Turkish News Articles: A Machine Learning Approach

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

The effect of thrombosis-related laboratory values on mortality in COVID-19 infection

OBJECTIVE: COVID-19 may cause thrombosis in both venous and arterial systems. Familiarity with the signs and symptoms of thrombosis and its treatment is essential in treating COVID-19 infection and its complications. D-Dimer and mean platelet volume (MPV) are measurements related to the development of thrombosis. This study investigates whether MPV and D-Dimer values could be used to determine the risk of thrombosis and mortality in the COVID-19 early stages. PATIENTS AND METHODS: 424 patients who were COVID-19 positive, according to the World Health Organization (WHO) guidelines, were randomly and retrospectively included in the study. Demographic and clinical characteristics such as age, gender, and length of hospitalization were obtained from the digital records of participants. Participants were divided into living and deceased groups. The patients’ biochemical, hormonal, and hematological parameters were analyzed retrospectively. RESULTS: White blood cells (WBC), neutrophils, and monocytes were significantly different in the two groups (p-value <0.001), and their values were lower in the living group than in the deceased group. MPV median values did not differ according to prognosis (p-value = 0.994). While the median value was 9.9 in the survivors, it was 10 in the deceased. Creatinine, procalcitonin, ferritin, and the number of hospitalization days in living patients were significantly lower than in patients who died (p-value <0.001). Median values of D-dimer (mg/L) differ according to prognosis (p-value <0.001). While the median value was 0.63 in the survivors, it was found as 438 in the deceased. CONCLUSIONS: Our results did not show any significant relationship between the mortality of COVID-19 patients and their MPV levels. However, a significant association between D-Dimer and mortality in COVID-19 patients was observed