Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2025In-depth review of industry 4.0: research implications and future perspectives; [Endüstri 4.0'ın derinlemesine incelemesi: araştırma sonuçları ve gelecek perspektifleri]
Gurcan, Fatih; Cam, Handan; Ahmed, Sana
The term “Industry 4.0” signifies a pivotal stage in industrial development, representing the current wave of transformation characterized by the integration of digital technologies into manufacturing and business processes. This concept spans multiple disciplines, including industrial engineering, data science, marketing, and strategic management. While a considerable volume of research has been conducted under the umbrella of Industry 4.0, there remains a lack of a comprehensive synthesis that captures the full scope and evolution of the field. The objective of this study is to systematically analyze, classify, and evaluate published research related to Industry 4.0, while identifying key gaps and forecasting potential future research directions. To accomplish this, a total of 10,696 journal articles indexed in the SCOPUS database from 2013 to 2023 were analyzed. The Latent Dirichlet Allocation (LDA) algorithm—a widely used probabilistic topic modeling technique in text mining—was applied to uncover latent themes within the literature. The analysis revealed 41 major topics, which were subsequently organized into five overarching categories: Sustainability, Manufacturing, Technology, Process, and Management. Findings indicate a notable shift in research emphasis over the past decade—from predominantly technical and engineering-focused issues toward broader concerns involving process optimization and organizational management. This transition highlights the growing importance of interdisciplinary approaches in addressing the complex challenges posed by Industry 4.0. Ultimately, this study not only maps the intellectual structure of existing research but also serves as a valuable resource for scholars, helping to navigate emerging trends and target underexplored areas in future investigations. This comprehensive thematic overview supports academics in pinpointing neglected subject areas, aligning future investigations with emerging priorities, and refining the strategic focus of Industry 4.0 research agendas.
- MakaleAçık Erişim2024Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers
Cam, Handan; Cam, Alper Veli; Demirel, Ugur; Ahmed, Sana
This paper presents a sentiment analysis combining the lexicon-based and machine learning (ML)-based approaches in Turkish to investigate the public mood for the prediction of stock market behavior in BIST30, Borsa Istanbul. Our main motivation behind this study is to apply sentiment analysis to financial-related tweets in Turkish. We import 17189 tweets posted as "#Borsaistanbul, #Bist, #Bist30, #Bist100″ on Twitter between November 7, 2022, and November 15, 2022, via a MAXQDA 2020, a qualitative data analysis program. For the lexicon-based side, we use a multilingual sentiment offered by the Orange program to label the polarities of the 17189 samples as positive, negative, and neutral labels. Neutral labels are discarded for the machine learning experiments. For the machine learning side, we select 9076 data as positive and negative to implement the classification problem with six different supervised machine learning classifiers conducted in Python 3.6 with the sklearn library. In experiments, 80 % of the selected data is used for the training phase and the rest is used for the testing and validation phase. Results of the experiments show that the Support Vector Machine and Multilayer Perceptron classifier perform better than other classifiers with 0.89 and 0.88 accuracy and AUC values of 0.8729 and 0.8647 respectively. Other classifiers obtain approximately a 78,5 % accuracy rate. It is possible to increase sentiment analysis accuracy with parameter optimization on a larger, cleaner, and more balanced dataset by changing the pre-processing steps. This work can be expanded in the future to develop better sentiment analysis using deep learning approaches.