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Yayın Turkish sentiment analysis: a comprehensive review(Yildiz Technical University, 2024-08) Altınel Girgin, Ayşe Berna; Gümüşçekiçci, Gizem; Birdemir, Nuri CanSentiment analysis (SA) is a very popular research topic in the text mining field. SA is the process of textual mining in which the meaning of a text is detected and extracted. One of the key aspects of SA is to analyze the body of a text to determine its polarity to understand the opinions it expresses. Substantial amounts of data are produced by online resources such as social media sites, blogs, news sites, etc. Due to this reason, it is impossible to process all of this data without automated systems, which has contributed to the rise in popularity of SA in recent years. SA is considered to be extremely essential, mostly due to its ability to analyze mass opinions. SA, and Natural Language Processing (NLP) in particular, has become an overwhelmingly popular topic as social media usage has increased. The data collected from social media has sourced numerous different SA studies due to being versatile and accessible to the masses. This survey presents a comprehensive study categorizing past and present studies by their employed methodologies and levels of sentiment. In this survey, Turkish SA studies were categorized under three sections. These are Dictionary-based, Machine Learning-based, and Hybrid-based. Researchers can discover, compare, and analyze properties of different Turkish SA studies reviewed in this survey, as well as obtain information on the public dataset and the dictionaries used in the studies. The main purpose of this study is to combine Turkish SA approaches and methods while briefly explaining its concepts. This survey uniquely categorizes a large number of related articles and visualizes their properties. To the best of our knowledge, there is no such comprehensive and up-to-date survey that strictly covers Turkish SA which mainly concerns analysis of sentiment levels. Furthermore, this survey contributes to the literature due to its unique property of being the first of its kind.Yayın Text-to-SQL: a methodical review of challenges and models(TÜBİTAK, 2024-05-20) Kanburoğlu, Ali Buğra; Tek, Faik BorayThis survey focuses on Text-to-SQL, automated translation of natural language queries into SQL queries. Initially, we describe the problem and its main challenges. Then, by following the PRISMA systematic review methodology, we survey the existing Text-to-SQL review papers in the literature. We apply the same method to extract proposed Text-to-SQL models and classify them with respect to used evaluation metrics and benchmarks. We highlight the accuracies achieved by various models on Text-to-SQL datasets and discuss execution-guided evaluation strategies. We present insights into model training times and implementations of different models. We also explore the availability of Text-to-SQL datasets in non-English languages. Additionally, we focus on large language model (LLM) based approaches for the Text-to-SQL task, where we examine LLM-based studies in the literature and subsequently evaluate the LLMs on the cross-domain Spider dataset. Finally, we conclude with a discussion of future directions for Text-to-SQL research, identifying potential areas of improvement and advancements in this field.Yayın LaSIPDE: Latent-Space Identification of Partial Differential Equations from indirect, high-dimensional measurements(Frontiers Media SA, 2026-04-14) Koulali, Imane; Turan, Erhan; Eskil, Mustafa TanerDiscovering governing equations from data is a central challenge in scientific machine learning, particularly when observations are high-dimensional and the underlying state variables are not directly accessible. In this work, we introduce a framework for data-driven discovery of partial differential equations (PDEs) from indirect high-dimensional observations. The proposed approach combines nonlinear representation learning through an autoencoder with sparse identification of governing equations in the latent space, enabling simultaneous model reduction and PDE discovery while preserving spatial structure. Unlike existing methods that either operate on observable variables or discover latent ordinary differential equations, our framework identifies PDEs directly in the learned latent coordinates. We validate the approach on high-dimensional observations generated from Burgers and Korteweg-de Vries (KdV) systems, where the true state variables are intentionally hidden. In both cases, the method successfully recovers the correct dynamical operators, including diffusion, nonlinear advection, and dispersive terms. Although the recovered coefficients differ due to latent coordinate transformations, we show both theoretically and empirically that the discovered equations are dynamically equivalent to the ground-truth systems up to an affine transformation. These results demonstrate that governing PDEs can be recovered from indirect, high-dimensional data without access to the physical state variables, providing a foundation for interpretable model discovery in realistic measurement settings.
