Using text representation and deep learning methods for turkish text classification

The heavy use of the Internet has led to a significant increase in the amount of text content produced in online platforms. Huge amount of online textual data is difficult to process, and new techniques have begun to be developed to process online data automatically. New word and document representation methods and deep learning-based classifiers have emerged recently to work with large text datasets as an alternative way to traditional text processing methods. The vast majority of studies using these methods were done with English texts. For Turkish texts, these methods have been used in the last 2 or 3 years. In this thesis, our aim is to evaluate the performances of new text representation and deep learning-based methods on classification of Turkish texts having different characteristics to show the usability of these methods on different document types. Therefore, these methods are used for the problems of sentiment and document classification and their performances are compared with traditional text classification methods. In order to make performance comparisons of the classifiers for the two text classification tasks that studied, deep learning-based convolutional neural networks and long short-term memory networks are used; as well as traditional classifiers which frequently used for Turkish texts in the literature. In the experimental evaluations it is found that embedding methods have similar performance with the traditional tf and tf-idf weighting methods, and in some cases achieve higher classification success. Deep learning-based classifiers have equal or higher classification success than the traditional classifiers.