Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 1Words Speak Louder Than Actions: Decoding Emotions Through NLP(Institute of Electrical and Electronics Engineers Inc., 2024-10-26) Paksoy, Melda; Bakal, GokhanEmotion detection in text remains a significant challenge in Natural Language Processing due to human emotions' complexity and subtle nuances. This paper presents multiple experimental models for emotion classification using an up-to-date dataset curated to address 13 emotions implied in Twitter posts. We evaluated various machine learning (ML) models, including Logistic Regression, Random Forest, SVM, and XGBoost, alongside deep learning (DL) architectures such as LSTM and CNN. Our results demonstrate the efficacy of deep learning models, particularly the CNN model by achieving an impressive F1 score of 0.99. This study contributes to emotion detection capabilities, paving the way for more nuanced and accurate sentiment analysis (SA) in various text analysis applications. © 2025 Elsevier B.V., All rights reserved.Conference Object Text Classification Experiments on Contextual Graphs Built by N-Gram Series(Springer International Publishing AG, 2025) Sen, Tarik Uveys; Yakit, Mehmet Can; Gumus, Mehmet Semih; Abar, Orhan; Bakal, GokhanTraditional n-gram textual features, commonly employed in conventional machine learning models, offer lower performance rates on high-volume datasets compared to modern deep learning algorithms, which have been intensively studied for the past decade. The main reason for this performance disparity is that deep learning approaches handle textual data through the word vector space representation by catching the contextually hidden information in a better way. Nonetheless, the potential of the n-gram feature set to reflect the context is open to further investigation. In this sense, creating graphs using discriminative ngram series with high classification power has never been fully exploited by researchers. Hence, the main goal of this study is to contribute to the classification power by including the long-range neighborhood relationships for each word in the word embedding representations. To achieve this goal, we transformed the textual data by employing n-gram series into a graph structure and then trained a graph convolution network model. Consequently, we obtained contextually enriched word embeddings and observed F1-score performance improvements from 0.78 to 0.80 when we integrated those convolution-based word embeddings into an LSTM model. This research contributes to improving classification capabilities by leveraging graph structures derived from discriminative n-gram series.Conference Object Citation - Scopus: 10On Comparative Classification of Relevant COVID-19 Tweets(Institute of Electrical and Electronics Engineers Inc., 2021-09-15) Bakal, Gokhan; Abar, OrhanDue to the impressive information dissemination power of social networks such as Twitter, people tend to check social networks and Web pages more than other traditional news sources, including newspapers, TV news programs, or radio channels. In that sense, the information carried by the content of the shared social media posts becomes much more considerable. However, most of the posts are commonly either irrelevant or inaccurate. Besides, the more critical case than the correctness of the information is the diffusion speed on Twitter through the reply or retweet actions. These activities make the initial situation even more complicated than itself due to the unregulated nature of the social networks and the lack of an immediate verification mechanism for the correctness of the posts. When we consider the current Covid-19 pandemic period (causing the coronavirus disease), one of the most utilized information resources is Twitter except the official health administration institutions. Thereupon, examining the correctness of the information related to the Covid-19 pandemic by computational techniques (e.g., Data Mining, Machine Learning, and Deep Learning) has been gaining popularity and remains a substantial task. Hence, we mainly focused on analyzing the correctness of the posts related to the current pandemic shared on the Twitter platform. Therefore, the overall goal of this work is to classify the relevant tweets using linear and non-linear machine learning models. We achieved the best F1 performance score (99%) with the neural network model using the unigram features & threshold value of 50 among all model configurations. © 2022 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 2NLP-Driven Fake News Detection: A Machine Learning Perspective(IEEE, 2025-05-23) Coban, Mert Korkut; Bakal, GokhanThe rapid spread of fake news poses a significant challenge, impacting public opinion, decision-making, and societal trust. This study explores the application of Natural Language Processing (NLP) and Machine Learning (ML) techniques for robust fake news detection. Using datasets such as ISOT Fake News, WELFake, and Football Fake News, the project employs advanced preprocessing methods and feature extraction techniques, including TF-IDF, Word2Vec, and GloVe. A comprehensive evaluation of machine learning models-Random Forest, Support Vector Machines (SVM), and Neural Networks-was conducted to identify the optimal configuration. Results demonstrate that Random Forest with TF-IDF excels in in-domain detection, achieving an F1-score of 99.70%, while Neural Networks paired with Word2Vec and GloVe embeddings outperform in cross-dataset scenarios. The study highlights the importance of dataset size, domain relevance, and feature representation in achieving high generalizability. These findings provide a scalable framework for combating misinformation on digital platforms.Conference Object Graph-Based Biomedical Knowledge Discovery(IEEE, 2024-05-15) Altuner, Osman; Bakir-Gungor, Burcu; Bakal, GokhanThe digitalization process is progressing at a very high speed all over the world. While this situation provides many conveniences in today's life, it also brings along a problem such as analyzing and processing the huge digital data. This also applies to published academic studies. In this sense, the process of evaluating each study to access previously unknown information within the studies requires a very laborious process. For this reason, in this study, the publications obtained for the target diseases were analyzed by text analysis processes and converted into a graph structure that enables the linking of meaningful terms through biomedical relationships. On the dense graph structure obtained, binary biomedical entities with important links such as treats, causes, associated_with were queried. The entity pairs obtained according to the query results were also confirmed by manual search method and proved to be real connections. In this study, retrieval of known biomedical entities with the proposed approach solved the time-consuming manual search problem. There is also the potential to obtain unknown/unexplored possible new relationships (e.g., therapeutic, causal, etc.) with multiple binary linking patterns.Conference Object Citation - Scopus: 1From Traditional to Deep: Evaluating Sentiment Analysis Models on a Large-Scale Tweet Dataset(Institute of Electrical and Electronics Engineers Inc., 2024-10-26) Mammadov, Alisahib; Bakal, GokhanThis study investigates the effectiveness of various machine learning (ML) and deep learning (DL) techniques for large-scale sentiment analysis on Twitter data. We leverage a publicly available dataset of one million tweets, annotated with four sentiment labels (positive, negative, uncertainty, and liti-gious), to train and evaluate a range of models. Our experiments demonstrate that traditional ML algorithms, particularly XG-Boost, achieve high performance, with the best F1 score reaching 95.81% using a combination of unigrams and bigrams. Among DL models, a hybrid CNN-BiGRU architecture yields the highest average F1 score of 95.42%. Our findings highlight the strengths of different approaches for sentiment analysis on Twitter data and emphasize the importance of data preprocessing and model selection for achieving optimal performance. © 2025 Elsevier B.V., All rights reserved.Article Citation - WoS: 8Citation - Scopus: 7Enhancing Sentiment Analysis in Stock Market Tweets Through Bert-Based Knowledge Transfer(Springer, 2025-02-26) Cicekyurt, Emre; Bakal, GokhanOne of the widely studied text classification efforts is sentiment analysis. It is a specific examination involving natural language processing and machine learning methods to understand semantic orientation from textual data. Working social media posts, such as tweets, for sentiment analysis, is quite common among researchers due to the speed of information dissemination. In this regard, forecasting stock market tweets is a widely studied research topic. Some studies have revealed a strong connection between sentiment and stock market performance, while others have not found any notable associations. The proposed work shows two distinct approaches to sentiment analysis over the stock market tweets. The first approach employs traditional machine learning algorithms, including logistic regression, random forest, and XGBoost. The second approach constructs deep learning (as a subfield of machine learning) models using LSTM and CNN algorithms to classify the test instances into positive, negative, or neutral classes through ten randomly shuffled data splits. In this study, the labeled data size is gradually increased utilizing a pre-trained model, FinBERT. It is exclusively employed to label unlabeled data instances to integrate them into the experiments. The goal is to monitor the effect of the additional newly-labeled examples on the sentiment analysis performance. The experiments showed that the average F1-score improved by 20% for the deep learning models and 17% for the machine learning models. In the end, the paper reveals a strong positive correlation between training data size and the classification performance of the experimental approaches.Article Citation - WoS: 6Citation - Scopus: 9Combining N-Grams and Graph Convolution for Text Classification(Elsevier, 2025-05) Sen, Tarik Uveys; Yakit, Mehmet Can; Gumus, Mehmet Semih; Abar, Orhan; Bakal, GokhanText classification, a cornerstone of natural language processing (NLP), finds applications in diverse areas, from sentiment analysis to topic categorization. While deep learning models have recently dominated the field, traditional n-gram-driven approaches often struggle to achieve comparable performance, particularly on large datasets. This gap largely stems from deep learning' s superior ability to capture contextual information through word embeddings. This paper explores a novel approach to leverage the often-overlooked power of n-gram features for enriching word representations and boosting text classification accuracy. We propose a method that transforms textual data into graph structures, utilizing discriminative n-gram series to establish long-range relationships between words. By training a graph convolution network on these graphs, we derive contextually enhanced word embeddings that encapsulate dependencies extending beyond local contexts. Our experiments demonstrate that integrating these enriched embeddings into an long-short term memory (LSTM) model for text classification leads to around 2% improvements in classification performance across diverse datasets. This achievement highlights the synergy of combining traditional n-gram features with graph-based deep learning techniques for building more powerful text classifiers.Article Citation - Scopus: 10Building a Challenging Medical Dataset for Comparative Evaluation of Classifier Capabilities(Elsevier Ltd, 2024-08) Bozkurt, Berat; Coskun, Kerem; Bakal, GokhanSince the 2000s, digitalization has been a crucial transformation in our lives. Nevertheless, digitalization brings a bulk of unstructured textual data to be processed, including articles, clinical records, web pages, and shared social media posts. As a critical analysis, the classification task classifies the given textual entities into correct categories. Categorizing documents from different domains is straightforward since the instances are unlikely to contain similar contexts. However, document classification in a single domain is more complicated due to sharing the same context. Thus, we aim to classify medical articles about four common cancer types (Leukemia, Non-Hodgkin Lymphoma, Bladder Cancer, and Thyroid Cancer) by constructing machine learning and deep learning models. We used 383,914 medical articles about four common cancer types collected by the PubMed API. To build classification models, we split the dataset into 70% as training, 20% as testing, and 10% as validation. We built widely used machine-learning (Logistic Regression, XGBoost, CatBoost, and Random Forest Classifiers) and modern deep-learning (convolutional neural networks - CNN, long short-term memory - LSTM, and gated recurrent unit - GRU) models. We computed the average classification performances (precision, recall, F-score) to evaluate the models over ten distinct dataset splits. The best-performing deep learning model(s) yielded a superior F1 score of 98%. However, traditional machine learning models also achieved reasonably high F1 scores, 95% for the worst-performing case. Ultimately, we constructed multiple models to classify articles, which compose a hard-to-classify dataset in the medical domain. © 2024 Elsevier B.V., All rights reserved.Article Citation - WoS: 18Citation - Scopus: 13An Empirical Study of Sentiment Analysis Utilizing Machine Learning and Deep Learning Algorithms(Springernature, 2023-12-09) Erkantarci, Betul; Bakal, GokhanAmong text-mining studies, one of the most studied topics is the text classification task applied in various domains, including medicine, social media, and academia. As a sub-problem in text classification, sentiment analysis has been widely investigated to classify often opinion-based textual elements. Specifically, user reviews and experiential feedback for products or services have been employed as fundamental data sources for sentiment analysis efforts. As a result of rapidly emerging technological advancements, social media platforms such as Twitter, Facebook, and Reddit, have become central opinion-sharing mediums since the early 2000s. In this sense, we build various machine-learning models to solve the sentiment analysis problem on the Reddit comments dataset in this work. The experimental models we constructed achieve F1 scores within intervals of 73-76%. Consequently, we present comparative performance scores obtained by traditional machine learning and deep learning models and discuss the results.