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Öğe ANALYSIS OF DATA PRE-PROCESSING METHODS FOR SENTIMENT ANALYSIS OF REVIEWS(Agh Univ Science & Technology Press, 2019) Parlar, Tuba; Ozel, Selma Ayse; Song, FeiThe goals of this study are to analyze the effects of data pre-processing methods for sentiment analysis and determine which of these pre-processing methods (and their combinations) are effective for English as well as for an agglutinative language like Turkish. We also try to answer the research question of whether there are any differences between agglutinative and non-agglutinative languages in terms of pre-processing methods for sentiment analysis. We find that the performance results for the English reviews are generally higher than those for the Turkish reviews due to the differences between the two languages in terms of vocabularies, writing styles, and agglutinative property of the Turkish language.Öğe Bloklara ayrılmış matrislerin Khatri-Rao ve Tracy-Singh çarpımları için algoritmalar(Hatay Mustafa Kemal Üniversitesi, 2010) Parlar, Tuba; İpek, AhmetKhatri-Rao çarpımı ve Tracy-Singh çarpımı sırasıyla genelleştirilmiş Hadamard çarpımı ve Kronecker çarpımı olarak bilinmektedir. Bu çalışmada, bloklara ayrılmış matrislerin Khatri-Rao ve Tracy-Singh çarpımlarını bilgisayar ortamında hesaplayan MATLAB algoritması tanımlanmakta, algoritmanın akış çizelgesi verilerek algoritma adımsal olarak anlatılmaktadır. Son olarak, algoritma örneklerle test edilmektedir.Öğe Comparison of Feature Selection Methods for Sentiment Analysis on Turkish Twitter Data(Ieee, 2017) Parlar, Tuba; Sarac, Esra; Ozel, Selma AyseThe Internet and social media provide a major source of information about people's opinions. Due to the rapidly growing number of online documents, it becomes both time-consuming and hard task to obtain and analyze the desired opinionated information. Sentiment analysis is the classification of sentiments expressed in documents. To improve classification perfromance feature selection methods which help to identify the most valuable features are generally applied. In this paper, we compare the performance of four feature selection methods namely Chi-square, Information Gain, Query Expansion Ranking, and Ant Colony Optimization using Maximum Entropi Modeling classification algorithm over Turkish Twitter dataset. Therefore, the effects of feature selection methods over the performance of sentiment analysis of Turkish Twitter data are evaluated. Experimental results show that Query Expansion Ranking and Ant Colony Optimization methods outperform other traditional feature selection methods for sentiment analysis.Öğe Data Privacy and Security in the Metaverse(Springer Science and Business Media Deutschland GmbH, 2023) Parlar, TubaMetaverse is an abstract concept that transforms our physical world into a digital environment. As the Metaverse expands and gains widespread attention from users, privacy and security issues come to the forefront. An increase in the number of users means a large amount of personal data is being collected about users. Metaverse data includes biometric information, which consists of users’ physiological responses, facial expressions, voice tones, and vital characteristics. Artificial intelligence methods with biometric data raise concerns about data privacy and security. Limitations are required to be put on the type, amount of collected personal data, and how it will be shared with third parties. The use of wearable technologies also increases the effects of existing threats in the virtual world through new methods. Current security measures are insufficient for Metaverse applications. In this chapter, the threats and challenges faced in terms of data privacy and security in Metaverse applications are introduced, and methods developed as solutions to these fundamental problems are examined. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.Öğe A DEEP TRANSFER LEARNING FRAMEWORK for the STAGING of DIABETIC RETINOPATHY(2022) Çınarer, Gökalp; Kılıç, Kazım; Parlar, TubaDiabetes is a highly prevalent and increasingly common health disorder, resulting in health complications such as vision loss. Diabetic retinopathy (DR) is the most common form of diabetes-caused eye disease. Early diagnosis and treatment are crucial to prevent vision loss. DR is a progressive disease composed of five stages. The accurate diagnosis of DR stages is highly important in guiding the treatment process. In this study, we propose a deep transfer learning framework for automatic detection of DR stages. We examine our proposed model by comparing different convolutional neural networks architectures: VGGNet19, DenseNet201, and ResNet152. Our results demonstrate better accuracy after applying transfer learning and hyper-parameter tuning to classify the fundus images. When the general test accuracy and the performance evaluations are compared, the DenseNet201 model is observed with the highest test accuracy of 82.7%. Among the classification algorithms, the highest AUC value is 94.1% obtained with RestNet152.Öğe Interactions Between Term Weighting and Feature Selection Methods on the Sentiment Analysis of Turkish Reviews(Springer International Publishing Ag, 2018) Parlar, Tuba; Ozel, Selma Ayse; Song, FeiTerm weighting methods assign appropriate weights to the terms in a document so that more important terms receive higher weights for the text representation. In this study, we consider four term weighting and three feature selection methods and investigate how these term weighting methods respond to the reduced text representation. We conduct experiments on five Turkish review datasets so that we can establish baselines and compare the performance of these term weighting methods. We test these methods on the English reviews so that we can identify their differences with the Turkish reviews. We show that both tf and tp weighting methods are the best for the Turkish, while tp is the best for the English reviews. When feature selection is applied, tf * idf method with DFD and chi(2) has the highest accuracies for the Turkish, while tf * idf and tp methods with chi(2) have the best performance for the English reviews.Öğe An investigation of term weighting and feature selection methods for sentiment analysis(Islamic Azad University, 2018) Parlar, Tuba; Özel, Selma AyseSentiment analysis automatically classifies the opinions, which are expressed in a document, usually as positive or negative. A review document in general, reflects its author's opinion about the objects mentioned in the text. Therefore, it can have many useful applications such as opinionated web search and automatic analysis of reviews. Although sentiment analysis is a kind of text classification problem, structures of review documents are different from texts like news, articles, or web pages; so that techniques applied for text classification are needed to be re-experimented for the sentiment analysis. Assigning appropriate weights to features is important to the performance of sentiment analysis so that important features can receive higher weights for the feature vectors. Feature selection reduces feature vector size by eliminating redundant or irrelevant features to improve classification accuracy. In this study, our aim is to examine the effects of term weighting methods on newly proposed Query Expansion Ranking (QER) feature selection method and also compare the classification results with one of the well-known feature selection method namely Chi-square statistic. We use three popular term weighting methods (i.e., term presence, term frequency, term frequency and inverse document frequency-tf*idf) and perform experiments using multinomial Naïve Bayes classifier. The experimental results show that when QER feature selection method is used with tf*idf term weighting method, the classification performance improves in terms of F-score. © 2017 Majlesi Branch, Islamic Azad University.Öğe IWD Based Feature Selection Algorithm for Sentiment Analysis(Kaunas Univ Technology, 2019) Parlar, Tuba; Sarac, EsraFeature selection methods aim to improve the classification performance by eliminating non-valuable features. In this paper, our aim is to apply a recent optimization technique namely the Intelligent Water Drops (IWD) algorithm to select best features for sentiment analysis. We investigate the classification performances of our proposed IWD based feature selection method by comparing one of the well-known feature selection method using Maximum Entropy classifier. Experimental results show that Intelligent Water Drops based feature selection method outperforms than ReliefF method for sentiment analysis.Öğe A New Feature Selection Method for Sentiment Analysis of Turkish Reviews(Ieee, 2016) Parlar, Tuba; Ozel, Selma AyseSentiment analysis identifies people's opinions, sentiments about a product, a service, an organization, or an event. Because of huge review documents, researchers explore different feature selection methods that aim to eliminate non valuable features. However, not much work has been done on feature selection methods for sentiment analysis of Turkish reviews. In this study, we propose a new feature selection method called Query Expansion Ranking that is based on query expansion term weighting methods, which are used in Information Retrieval domain to determine the most valuable terms for query expansion. We compare Query Expansion Ranking with Chi Square method, which is a well-known and successful feature selector, and Document Frequency Difference which is a feature selection method proposed for sentiment analysis of English reviews. Experiments are conducted on four Turkish product review datasets that are book, DVDs, electronics, and kitchen appliances reviews by using a supervised machine learning classification method, namely Naive Bayes Multinomial classifier. We show that our new proposed method improves sentiment analysis performance in terms of classification accuracy and time. In the experimental evaluation, we also show that our new feature selector improves classification accuracy better than Chi Square, and Document Frequency Difference methods.Öğe Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT(Public Library Science, 2023) Bulut, Gulcan; Atilgan, Hasan Ikbal; Cinarer, Gokalp; Kilic, Kazim; Yikar, Deniz; Parlar, TubaObjectivesThe aim of the study is 18F-FDG PET/CT imaging by using deep learning method are predictive for pathological complete response pCR after Neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC).IntroductionNAC is the standard treatment for locally advanced breast cancer (LABC). Pathological complete response (pCR) after NAC is considered a good predictor of disease-free survival (DFS) and overall survival (OS).Therefore, there is a need to develop methods that can predict the pCR at the time of diagnosis.MethodsThis article was designed as a retrospective chart study.For the convolutional neural network model, a total of 355 PET/CT images of 31 patients were used. All patients had primary breast surgery after completing NAC.ResultsPathological complete response was obtained in a total of 9 patients. The study results show that our proposed deep convolutional neural networks model achieved a remarkable success with an accuracy of 84.79% to predict pathological complete response.ConclusionIt was concluded that deep learning methods can predict breast cancer treatment.Öğe QER: a new feature selection method for sentiment analysis(Korea Information Processing Soc, 2018) Parlar, Tuba; Ozel, Selma Ayse; Song, FeiSentiment analysis is about the classification of sentiments expressed in review documents. In order to improve the classification accuracy, feature selection methods are often used to rank features so that non-informative and noisy features with low ranks can be removed. In this study, we propose a new feature selection method, called query expansion ranking, which is based on query expansion term weighting methods from the field of information retrieval. We compare our proposed method with other widely used feature selection methods, including Chi square, information gain, document frequency difference, and optimal orthogonal centroid, using four classifiers: na < ve Bayes multinomial, support vector machines, maximum entropy modelling, and decision trees. We test them on movie and multiple kinds of product reviews for both Turkish and English languages so that we can show their performances for different domains, languages, and classifiers. We observe that our proposed method achieves consistently better performance than other feature selection methods, and query expansion ranking, Chi square, information gain, document frequency difference methods tend to produce better results for both the English and Turkish reviews when tested using na < ve Bayes multinomial classifier.Öğe Yönetim bilişim sistemleri ve örnek bir uygulama(Hatay Mustafa Kemal Üniversitesi, 2000) Parlar, Tuba; Bilgin, FevziVI ÖZET Günümüzde rekabetin yoğunlaşması, işletmeleri, müşterileri ve tedarikçileri ile bağlantılarım bütünleşik bir yapı içerisinde ele almaya zorlamaktadır. Hızlı küreselleşme hareketleri, bilişim teknolojilerinde yaşanan gelişmeler ve yoğun rekabet ile işletmeler geleneksel eski yöntemlerini hızla bırakma ve işlevsel süreçlerini bilişim teknolojileri ile güçlendirme çalışmalarına başlamışlardır. Bilişim teknolojilerinde yaşanan gelişmeler, Yönetim Bilişim Sistemlerini kullanmayı gerekli kılmıştır. Yönetim Bilişim Sistemleri, organizasyonu yönetmek için gerekli bilgiyi sağlayan, işlevsel süreçler ile ilgili performansları izleyen, koordinasyonu sürdüren ve sürekli bilgi/bilişim sağlayan bir sistemdir. Bu noktada ortaya Kurumsal Kaynak Planlama kavramı çıkmıştır. Kurumsal Kaynak Planlama yazılımı finans ve insan kaynaklan departmanlarının işlevsel süreçlerini otomatikleştiren ve imalatçılara sipariş ve üretim çizelgesi oluşturma gibi süreçlerinde yardımcı olan bir dizi uygulamadır. KKP sistemi SAP' nin R/3 ü gibi son derece kompleks ve kurulumuyla kullanıcıların tüm süreçlerinde köklü değişiklikler yapan bir sistemdir. Bu çalışma ile Bilişim Teknolojilerinin günümüz organizasyonları için önemi ve gerekliliği Yönetim Bilişim Sistemleri ve Kurumsal Kaynak Planlama kavramları çerçevesinde ortaya konmaya çalışılmıştır. Kurumsal Kaynak Planlama, sektöründe en bilinenlerden olan SAP R/3 Sistemi uygulama yazılımı incelenerek bu kavram pekiştirilmeye çalışılmıştır.
