Abstract: Authorship attribution, genre classification, and recommendation systems based on literary content have become increasingly important in natural language processing (NLP). This thesis presents a system capable of both classification and content-based recommendation by representing book texts in a vector space. Comprehensive datasets of Turkish and English novels were compiled. All texts were lowercased and preprocessed to remove punctuation and special characters, then transformed into fixed-length vectors using the Doc2Vec algorithm. To ensure generalization, a custom train/test split was applied, guaranteeing each author was represented in the test set. These document vectors were used for authorship and genre classification with classifiers such as Logistic Regression, LinearSVC, Random Forest, Gaussian Naive Bayes, and K-Nearest Neighbors. This study also adopts a centroid-based similarity approach that extends traditional cosine similarity principles. For each author and genre, a representative vector was computed from the training data, and test books were classified based on their angular proximity to these centroids. This approach improved conceptual sensitivity and yielded high accuracy. In the second phase, a content-based recommendation system was developed that, unlike collaborative filtering, relies solely on textual similarity. It recommends books with similar narrative styles to those already enjoyed by the reader, helping raise the visibility of lesser-known authors. This research shows that document embedding-based methods can be effectively applied in digital humanities, authorship attribution, and recommendation systems.
Abstract: In this thesis, a comprehensive performance comparison is presented on which large language model, vector database, embedding model and similarity method in a retrieval augmented generation system, in the setting of a Turkish supported conversation bot for the students in higher education. Although there are studies in this field for many languages, the number of studies conducted in the Turkish language and especially in the field of education is low. Therefore, there is a need to identify the best alternatives for the components of such systems when they are used under Turkish language rules. This study gives answers to this need which is obtained through scientifical experimentation and is practically realizable. The most commonly used, well known and reliable large language models, embedding algorithms associated with them, vector databases and similarity measures were selected and several question sets based on university regulations were tested on different combinations of them. Based on the achieved results and analysis made, the best combination was obtained. In this way, the optimal Turkish supported retrieval augmented generation based virtual academic advisor bot was created so that students can easily access what they are inquiring about academic rules. Furthermore, the workload of academics and administrative staff was lightened with this automation.
Abstract: The frequent occurrence of natural disasters, particularly seismic events, leads to significant destruction and brings major catastrophes. Addressing the critical need for fast information during such crises is of paramount importance. Therefore, this study focuses on mitigating disaster-related losses through an innovative system that processes information available on social platforms. Development and comparative evaluation of the components of a system designed to rapidly identify and prioritize vital information from high amounts tweet data are detailed. A specific dataset was compiled, consisting of tweets posted in the aftermath of the major earthquake in Türkiye in February 2023. Using this data, messages were classified into three primary categories: disaster-related and urgent, disaster-related but not urgent, and non-disaster-related, utilizing both prompt engineering and fine-tuning methodologies with large language models. Performance of Gemini-1.0-pro and GPT-4o-mini models, as well as fine-tuned versions of these models were compared. The results indicated that the fine-tuned GPT-4o-mini model, when used with advanced prompting strategies, achieved a classification success of 93.64%. Following this classification, an impact score algorithm was implemented. This algorithm utilizes various tweet engagement metrics, such as reposts, likes, and views, to further rank and highlight more significant messages. This research is anticipated to offer substantial support to rescue and aid organizations by enhancing their situational awareness and response capabilities. Furthermore, it aims to make a significant contribution to the advancement of Turkish language processing in the field of crisis informatics.