Turkish Text Classification Research Articles

Turkish Text Classification Based On Wrapper Feature Selection Using Particle Swarm Optimization

The vast majority of the digital era data is stored as text. Text mining is an integral part of data mining. Text classification (TC) is a natural language processing (NLP) operation often needed in text mining. This operation is needed in numerous kinds of research such as information retrieval, document classification, language detection, sentiment analysis, etc. According to the literature, the filter feature selection methods have often been applied to reduce the dimensionality of data in Turkish TC. However, the wrapper-based feature selection methods can provide better classification accuracies than the filter methods. Motivated by this idea, a Turkish TC method based on wrapper feature selection using particle swarm optimization algorithm (PSO) and multinomial naive bayes (MNB) classifier is proposed in this study. TTC-3600 Turkish news texts are used for TC in the experiments. The proposed method achieves a classification accuracy of 94.55% on TTC-3600 Turkish news text dataset by using stemming Tf-Idf features. Hence, it produces competitive accuracies to the cutting-edge Turkish TC methods.

An Analysis of Intelligent Turkish Text Classification Models for Routing Calls in Call Centers: A Case Study on the Republic of Turkiye Ministry of Trade Call Center

Call centers play a key role in the management of customer relationships in the modern business world. However, the growing demand for their services presents significant challenges, particularly in terms of staffing and handling increasing call volumes. This paper addresses these issues by presenting an AI-driven text classification framework tailored for the Republic of Turkiye Ministry of Trade Call Centre (MTCC), with the aim of automatically routing calls to relevant departments. Using a specific dataset of 20,000 phone call texts collected from the MTCC, the study employs TF-IDF, Word2Vec, and GloVe text vectorization techniques and applies various machine learning algorithms such as K-Nearest Neighbours, Naive Bayes, Support Vector Machines, Adaptive Boosting, Decision Tree and Random Forest for text classification. Through a comprehensive analysis, the study answers key research questions regarding optimal classifiers and vectorization methods. The proposed solution not only improves the efficiency of MTCC's call routing but also provides researchers with practical insights regarding Turkish text classification. The results indicate that a combination of the Random Forest classifier and Word2Vec text vectorization method is the optimal model that can manage to route calls in real-time.

Relational Turkish Text Classification Using Distant Supervised Entities and Relations

Relational Turkish Text Classification Using Distant Supervised Entities and Relations

Unified benchmark for zero-shot Turkish text classification

Effective learning schemes such as fine-tuning, zero-shot, and few-shot learning, have been widely used to obtain considerable performance with only a handful of annotated training data. In this paper, we presented a unified benchmark to facilitate the problem of zero-shot text classification in Turkish. For this purpose, we evaluated three methods, namely, Natural Language Inference, Next Sentence Prediction and our proposed model that is based on Masked Language Modeling and pre-trained word embeddings on nine Turkish datasets for three main categories: topic, sentiment, and emotion. We used pre-trained Turkish monolingual and multilingual transformer models which can be listed as BERT, ConvBERT, DistilBERT and mBERT. The results showed that ConvBERT with the NLI method yields the best results with 79% and outperforms previously used multilingual XLM-RoBERTa model by 19.6%. The study contributes to the literature using different and unattempted transformer models for Turkish and showing improvement of zero-shot text classification performance for monolingual models over multilingual models.

Improving automated Turkish text classification with learning‐based algorithms

AbstractText classification is the process of determining categories or tags of a document depending on its content. Although text classification is a well‐known process, it has many steps that require tuning to improve mathematical models. This article provides a novel methodology and expresses key points to improve text classification performance using learning‐based algorithms and techniques. First, to check the effectiveness of the proposed methodology, we selected two public Turkish news benchmarking datasets. Then, we performed extensive testing using both supervised machine learning algorithms and state‐of‐art pre‐trained language models. The experimental results show that our methodology outperforms previous news classification studies on these benchmarking datasets improving categorization results based on F1‐score. Therefore, we conclude that the presented methodology efficiently improves the classification results and selects the feasible classifier for a given dataset.

MooDetecTR: Kelime Vektörleri Vasıtasıyla Türkçe Şarkı Sözleri için Ruh Hali Tespiti

Along with the increasing use of online music platforms, catalogue-based searches have turned into mood-based seeking. In this study, we propose MooDetecTR, a semi-supervised learning framework that employs word vectors for Turkish song mood detection. In this framework, first word vectors are created through a large collection of textual data, which include more than 2.5 million Turkish documents, by using Word2Vec and GloVe algorithms. Subsequently, lyrics vectors are generated through combining already trained word vectors of the words in the lyrics selected for mood detection. Lastly, lyrics vectors are fed into various machine-learning algorithms as features to create models for music mood detection. For comparison, Turkish music mood detection is performed both via traditional bag-of-words model, with TF-IDF weights, and Doc2Vec algorithm. The effects of stemming of the words and stop-words removal on the results are investigated, as well. The best micro-f1 score (54.36%) obtained by the proposed framework is 3.81%, and 2.92% higher (7.54%, and 5.68% relative improvements) than the best score obtained from Doc2Vec and bag-of-words methods, respectively. Consequently, the results obtained show the effectiveness of incorporating word vectors generated using big textual data into Turkish text classification process, which is clearly illustrated by the improved classification performance.

Improving the accuracy using pre-trained word embeddings on deep neural networks for Turkish text classification

Today, extreme amounts of data are produced, and this is commonly referred to as Big Data. A significant amount of big data is composed of textual data, and as such, text processing has correspondingly increased in its importance. This is especially valid to the development of word embedding and other groundbreaking advancements in this field. However, When studies on text processing and word embedding are examined, it can be seen that while there have been many world language-oriented studies, especially for the English language, there has been an insufficient level of study undertaken specific to the Turkish language. As a result, Turkish was chosen as the target language for the current study. Two Turkish datasets were created for this study. Word vectors were trained using the Word2Vec method on an unlabeled large corpus of approximately 11 billion words. Using these word vectors, text classification was applied with deep neural networks on a second dataset of 1.5 million examples and 10 classes. The current study employed the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) methods – other types of this architecture – and their variations as deep neural network architectures. The performances of the embedding methods for the words used in this study, their effects on the rate of accuracy, and the success of the deep neural network architectures were then analyzed in detail. When studying the experimental results, it was determined that the GRU and LSTM methods were more successful compared to the other deep neural network models used in this study. The results showed that the pre-trained word vectors’ (PWVs) accuracy on deep neural networks improved at rates of approximately 5% and 7%. The datasets and word vectors of the current study will be shared in order to contribute to the Turkish language literature in this field.

Nitelik Çıkarımı Yöntemlerinin Türkçe Metinlerin Sınıflandırılmasına Etkisi

Feature extraction is the most important preprocessing step of text classification task. Effects of preprocessing techniques on text mining for English have been extensively studied. However, studies for Turkish are limited and generally belong to a specific problem domain. In this study, we investigate the effects of feature extraction techniques on four different Turkish text classification problems including news classification, spam e-mail detection, sentiment analysis, and author detection to show the differences and similarities among the problems. We also propose a new feature selection method to reduce feature space. The experimental analysis has showed that, stopword removal improves classification performance. However, stemming does not make any positive effect on classification accuracy. The most successful term weighting methods are tf and tf*idf. The proposed feature selection method improves classification performance and has higher accuracy than the well-known methods. 

The Effect of Ensemble Learning Models on Turkish Text Classification

The Effect of Ensemble Learning Models on Turkish Text Classification Due to rapid development of the Internet and related technologies, the amount of text-based content generated through Internet applications is increasing from day to day. Since text-based content is unstructured, accessing and managing this data is almost impossible. Consequently, there is a need for automatic text classification process. Text mining is a discipline in the Data Mining field and offers algorithms in order to perform text classification. The main objective of text classification is forming a learning model by using a training data set with pre-defined categories and placing data with unknown categories into correct categories. Different text classification algorithms such as decision trees, Bayesian classifiers, rule-based classifiers, neural networks, k-nearest neighbor classifier, support vector machines and ensemble learning methods exist in the literature. In this study, the effect of ensemble learning models on Turkish text classification was evaluated. A publicly available data set named TTC-3600 which consists of 3600 news collected from 6 news portals was selected. Text classification process was performed on TTC-3600 data set by using 4 base classification algorithms Naive Bayes, Support Vector Machine, K-Nearest Neighbor, J48 Decision tree and their Boosting, Bagging and Rotation Forest ensemble learning models. The experimental results shows that ensemble learning models generally give more accurate results by increasing the success of base classifiers.