Profile URL: https://hdl.handle.net/20.500.13091/12002
Job Title:Arş. Gör. Uzm.
Email Address:mcataltas@ktun.edu.tr
Main Affiliation:10.01. Department of Computer Engineering
Status: Current Staff
ORCID:
0000-0002-5598-9659
0000-0002-5598-9659Scopus ID:
57222720532
57222720532YÖK Akademik: 8D5408E398E8472D
Google Scholar:
6Iq6uOcAAAAJ
6Iq6uOcAAAAJWeb of Science ID:
JLM-6696-2023
JLM-6696-2023Name Variants:
Çataltaş, M. Ç., Mustafa
4 results
Scholarly Output Search Results
Now showing 1 - 4 of 4
Article Data Augmentation for Text Classification Using Autoencoders(IEEE-Inst Electrical Electronics Engineers Inc, 2025) Cataltas, Mustafa; Cicekli, Ilyas; Akhan Baykan, NurdanDeep learning models have greatly improved various natural language processing tasks. However, their effectiveness depends on large data sets, which can be difficult to acquire. To mitigate this challenge, data augmentation techniques are employed to artificially expand the training data by generating synthetic samples. By enriching the dataset, data augmentation enhances model generalization, reduces overfitting, and improves model performance. This paper investigates the effectiveness of employing autoencoders for text data augmentation to enhance the performance of text classification models. The research compares four types of autoencoders which are Traditional Autoencoder (AE), Adversarial Autoencoder (AAE), Denoising Adversarial Autoencoder (DAAE), and Variational Autoencoder (VAE). Basic text preprocessing techniques, which are lowercasing, removal of non-alphanumeric characters and removal of stop words, are applied to all documents. Additionally, label-based filtering is applied, where the outputs of autoencoders that contradict the predictions of BERT are eliminated. The experiments are conducted using the SST-2 sentiment classification dataset, which consists of 7,791 training instances and 1,821 test instances. To better analyze the impact of data augmentation methods, experiments are also performed on smaller subsets of 100, 200, 400, and 1,000 instances. Data augmentation is applied at ratios of 1:1, 1:2 and 1:4 for these subsets. The results demonstrate that AE-based data augmentation methods, particularly at a 1:1 ratio, achieve better accuracy than the baseline models. This underscores the potential of autoencoders in improving text classification outcomes in NLP tasks.Conference Object Academic Graph: a Literature Review System(Institute of Electrical and Electronics Engineers Inc., 2022-12-17) Çataltaş, M.; Yumuşak, S.; Oztoprak, K.As the number of academic publications increase, preparing a literature review becomes more challenging. This paper introduces an automated literature review support system to ease the literature review process for academia with reference graphs, abstract and full document summaries, paper clusters by keywords, abstracts, and abstract summaries combined. The output of the proposed system may ease exploring the state-of-the-art research. © 2022 IEEE.Article Citation - WoS: 1Sentiment Classification on Turkish Tweets About Covid-19 Using Lstm Network(Konya Teknik Univ, 2023) Çataltaş, Mustafa; Üstünel, Büşra; Baykan, Nurdan AkhanAs Covid-19 pandemic affected everyone in various aspects, people have been expressing their opinions on these aspects mostly on social media platforms because of the pandemic. These opinions play a crucial role in understanding the sentiments towards the pandemic. In this study, Turkish tweets on Covid-19 topic were collected from March 2020 to January 2021 and labelled as positive, negative, or neutral in terms of sentiment using BERT which is a pre-trained text classifier model. Using this labelled dataset, a set of experiments were carried out with SVM, Naive Bayes, K-Nearest Neighbors, and CNN-LSTM model machine learning algorithms for binary and multi-class classification tasks. Results of these experiments have shown that CNN-LSTM model outperforms other machine learning algorithms which are used in this study in both binary classification and multi-class classification tasks.Conference Object Comparison of Textual Data Augmentation Methods on Sst-2 Dataset(Springer Science and Business Media Deutschland GmbH, 2024) Çataltaş, M.; Baykan, N.A.; Cicekli, I.Since the arrival of advanced deep learning models, more successful techniques have been proposed, significantly enhancing the performance of nearly all natural language processing tasks. While these deep learning models achieve the best results, large datasets are needed to get these results. However, data collection in large amounts is a challenging task and cannot be done successfully for every task. Therefore, data augmentation might be required to satisfy the need for large datasets by generating synthetic data samples using original data samples. This study aims to give an idea to those who will work in this field by comparing the successes of using a large dataset as a whole and data augmentation in smaller pieces at different rates. For this aim, this study presents a comparison of three textual data augmentation techniques, examining their efficacy based on the augmentation mechanism. Through empirical evaluations on the Stanford Sentiment Treebank dataset, the sampling-based method LAMBADA showed superior performance in low-data regime scenarios and moreover showcased better results than other methods when the augmentation ratio is increased, offering significant improvements in model robustness and accuracy. These findings offer insights for researchers on augmentation strategies, thereby enhancing generalization in future works. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Research Topics
Domains
Physical Sciences
Fields
Computer Science
Subfields
Artificial Intelligence
Specific Research Areas
Topic Modeling
Sentiment Analysis and Opinion Mining
Natural Language Processing Techniques
Speech Recognition and Synthesis
Semantic Web and Ontologies
Sustainable Development Goals
1NO POVERTY
1
Research Products
Documents
4
Citations
5
h-index
1
Documents
3
Citations
3
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Konya Technical University | 4 |
| Konya Food and Agriculture University | 2 |
| KTO Karatay University | 2 |
| Hacettepe University | 2 |
1 / 1
Data obtained from OpenAlex
| Journal | Count |
|---|---|
| EAI/Springer Innovations in Communication and Computing | 1 |
| IEEE Access | 1 |
| Konya mühendislik bilimleri dergisi (Online) | 1 |
| Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 | 1 |
Current Page: 1 / 1
Scholarly Output
4
Articles
2
Views / Downloads
7/8
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
1
Scopus Citation Count
0
Patents
0
Projects
0
WoS Citations per Publication
0.25
Scopus Citations per Publication
0.00
Open Access Source
2
Supervised Theses
0
Scopus Quartile Distribution
Competency Cloud
