The exponential growth of digital data in recent years has spurred a significant interest in natural language processing (NLP) and sentiment analysis. However, the effectiveness of NLP models heavily relies on the availability of large, annotated datasets, which are often scarce or entirely absent for numerous languages, including Turkish. This scarcity of annotated data for Turkish presents a formidable obstacle in developing NLP models for the language. To overcome this challenge, various techniques have been proposed to augment the size of annotated datasets, with text data augmentation emerging as a promising solution. Text data augmentation involves the generation of synthetic data by transforming existing data, thus expanding the diversity and volume of the annotated dataset. While this technique has shown remarkable success in bolstering the performance of NLP models, its exploration in the context of Turkish and other low-resource languages has been limited. This paper introduces a novel ensemble approach to text data augmentation tailored for Turkish text sentiment classification. Our approach integrates both task-specific and universal transformations, capitalizing on the strengths of each to enrich the training dataset. We evaluate our proposed approach on the TRSAv1 dataset and compare it with established data augmentation techniques. The experimental results demonstrate that our ensemble method achieves superior accuracy in sentiment classification compared to conventional techniques. Additionally, we conduct an in-depth analysis to assess the impact of individual transformation functions on classification performance. Our contribution lies in bridging the gap in research on data augmentation techniques tailored to Turkish NLP, emphasizing the need for more advanced ensemble methods, and offering benchmarking results that pave the way for the development of precise NLP models not only for Turkish but also for other low-resource languages.

This paper introduces a novel classification method for multi-class multi-label datasets, named multi-class multi-label logistic model tree (MMLMT). Our approach supports multi-label learning to predict multiple class labels simultaneously, thereby enhancing the model’s capacity to capture complex relationships within the data. The primary goal is to improve the accuracy of classification tasks involving multiple classes and labels. MMLMT integrates the logistic regression (LR) and decision tree (DT) algorithms, yielding interpretable models with high predictive performance. By combining the strengths of LR and DT, our method offers a flexible and powerful framework for handling multi-class multi-label data. Extensive experiments demonstrated the effectiveness of MMLMT across a range of well-known datasets with an average accuracy of 85.90%. Furthermore, our method achieved an average of 9.87% improvement compared to the results of state-of-the-art studies in the literature. These results highlight MMLMT’s potential as a valuable approach to multi-label learning.

In a standard binary supervised classification task, the existence of both negative and positive samples in the training dataset are required to construct a classification model. However, this condition is not met in certain applications where only one class of samples is obtainable. To overcome this problem, a different classification method, which learns from positive and unlabeled (PU) data, must be incorporated. In this study, a novel method is presented: neighborhood-based positive unlabeled learning using decision tree (NPULUD). First, NPULUD uses the nearest neighborhood approach for the PU strategy and then employs a decision tree algorithm for the classification task by utilizing the entropy measure. Entropy played a pivotal role in assessing the level of uncertainty in the training dataset, as a decision tree was developed with the purpose of classification. Through experiments, we validated our method over 24 real-world datasets. The proposed method attained an average accuracy of 87.24%, while the traditional supervised learning approach obtained an average accuracy of 83.99% on the datasets. Additionally, it is also demonstrated that our method obtained a statistically notable enhancement (7.74%), with respect to state-of-the-art peers, on average.

Road traffic accident severity prediction is crucial for implementing effective safety measures and proactive traffic management strategies. Existing methods often treat this as a nominal classification problem and use traditional feature selection techniques. However, ordinal classification methods that account for the ordered nature of accident severity (e.g., slight < serious < fatal injuries) in feature selection still need to be investigated thoroughly. In this study, we propose a novel approach, the Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS), which utilizes the inherent ordering of class labels both in the feature selection and prediction stages for accident severity classification. The proposed approach enhances the model performance by separately determining feature importance based on severity levels. The experiments demonstrated the effectiveness of ORT-ROFS with an accuracy of 87.19%. According to the results, the proposed method improved prediction accuracy by 10.81% over state-of-the-art studies on average on different train–test split ratios. In addition, it achieved an average improvement of 4.58% in accuracy over traditional methods. These findings suggest that ORT-ROFS is a promising approach for accurate accident severity prediction, supporting road safety planning and intervention strategies.