| dc.description.abstract | Social media and microblogging platforms generally contain elements of figurative and nonliteral language,
including satire. The identification of figurative language is a fundamental task for sentiment analysis. It will not be
possible to obtain sentiment analysis methods with high classification accuracy if elements of figurative language have
not been properly identified. Satirical text is a kind of figurative language, in which irony and humor have been utilized
to ridicule or criticize an event or entity. Satirical news is a pervasive issue on social media platforms, which can be
deceptive and harmful. This paper presents an ensemble scheme for satirical news identification in Turkish news articles.
In the presented scheme, linguistic and psychological feature sets have been utilized to extract the feature sets (i.e.
linguistic, psychological, personal, spoken categories, and punctuation). In the classification phase, accuracy rates of
five supervised learning algorithms (i.e. naive Bayes algorithm, logistic regression, support vector machines, random
forest, and k-nearest neighbor algorithm) with three widely utilized ensemble methods (i.e. AdaBoost, bagging, and
random subspace) have been considered. Based on the results, we concluded that the random forest algorithm yielded
the highest performance, with a classification accuracy of 96.92% for satire detection in Turkish. For deep learning-based
architectures, we have achieved classification accuracy of 97.72% with the recurrent neural network architecture with
attention mechanism. | |
