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Exploring Graph-Based Techniques in Text Data Processing: A Comprehensive Survey of NLP Advancements
Review Article | Published: 18 February 2026
ICCK Transactions on Emerging Topics in Artificial Intelligence Volume 3, Issue 2, 2026: 86-127
Volume 3, Issue 2, ICCK Transactions on Emerging Topics in Artificial Intelligence
Volume 3, Issue 2, 2026
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ICCK Transactions on Emerging Topics in Artificial Intelligence, Volume 3, Issue 2, 2026: 86-127

Open Access | Review Article | 18 February 2026
Exploring Graph-Based Techniques in Text Data Processing: A Comprehensive Survey of NLP Advancements
by
Tuğba Çelikten Tuğba Çelikten 1,2 *
Aytuğ Onan Aytuğ Onan 3
1 Graduate School of Natural and Applied Sciences, Izmir Katip Çelebi University, Izmir 35620, Turkey
2 Department of Software Engineering, Faculty of Technology, Manisa Celal Bayar University, Manisa 45140, Turkey
3 Department of Computer Engineering, Izmir Institute of Technology, Izmir 35430, Turkey
* Corresponding Author: Tuğba Çelikten, [email protected]
DOI: 10.62762/TETAI.2025.740330
ARK: ark:/57805/tetai.2025.740330
Received: 22 November 2025, Accepted: 02 December 2025, Published: 18 February 2026  
   PDF  (2.62 MB)
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Abstract
Graph Neural Networks (GNNs) have become increasingly prominent in Natural Language Processing (NLP) due to their ability to model intricate relationships and contextual connections between texts. Unlike traditional NLP methods, which typically process text linearly, GNNs utilize graph structures to represent the complex relationships between texts more effectively. This capability has led to significant advancements in various NLP applications, such as social media interaction analysis, sentiment analysis, text classification, and information extraction. Notably, GNNs excel in scenarios with limited labeled data, often outperforming traditional approaches by providing deeper, context-aware solutions. Their versatility in handling different data types has made GNNs a popular choice in NLP research. In this study, we thoroughly explored the application of GNNs across various NLP tasks, demonstrating their advantages in understanding and representing text relationships. We also examined how GNNs address traditional NLP challenges, showcasing their potential to deliver more meaningful and accurate results. Our research underscores the value of GNNs as a potent tool in NLP and suggests future research directions to enhance their applicability and effectiveness further.
Graphical Abstract
Exploring Graph-Based Techniques in Text Data Processing: A Comprehensive Survey of NLP Advancements
Keywords
graph neural networks (GNN)
natural language processing (NLP)
graph convolutional networks (GCN)
heterogeneous graph neural networks
GNN-based NLP
Data Availability Statement
Not applicable.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
AI Use Statement
The authors declare that no generative AI was used in the preparation of this manuscript.
Ethical Approval and Consent to Participate
Not applicable.
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Çelikten, T., & Onan, A. (2026). Exploring Graph-Based Techniques in Text Data Processing: A Comprehensive Survey of NLP Advancements. ICCK Transactions on Emerging Topics in Artificial Intelligence, 3(2), 86–127. https://doi.org/10.62762/TETAI.2025.740330
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TY  - JOUR
AU  - Çelikten, Tuğba
AU  - Onan, Aytuğ
PY  - 2026
DA  - 2026/02/18
TI  - Exploring Graph-Based Techniques in Text Data Processing: A Comprehensive Survey of NLP Advancements
JO  - ICCK Transactions on Emerging Topics in Artificial Intelligence
T2  - ICCK Transactions on Emerging Topics in Artificial Intelligence
JF  - ICCK Transactions on Emerging Topics in Artificial Intelligence
VL  - 3
IS  - 2
SP  - 86
EP  - 127
DO  - 10.62762/TETAI.2025.740330
UR  - https://www.icck.org/article/abs/TETAI.2025.740330
KW  - graph neural networks (GNN)
KW  - natural language processing (NLP)
KW  - graph convolutional networks (GCN)
KW  - heterogeneous graph neural networks
KW  - GNN-based NLP
AB  - Graph Neural Networks (GNNs) have become increasingly prominent in Natural Language Processing (NLP) due to their ability to model intricate relationships and contextual connections between texts. Unlike traditional NLP methods, which typically process text linearly, GNNs utilize graph structures to represent the complex relationships between texts more effectively. This capability has led to significant advancements in various NLP applications, such as social media interaction analysis, sentiment analysis, text classification, and information extraction. Notably, GNNs excel in scenarios with limited labeled data, often outperforming traditional approaches by providing deeper, context-aware solutions. Their versatility in handling different data types has made GNNs a popular choice in NLP research. In this study, we thoroughly explored the application of GNNs across various NLP tasks, demonstrating their advantages in understanding and representing text relationships. We also examined how GNNs address traditional NLP challenges, showcasing their potential to deliver more meaningful and accurate results. Our research underscores the value of GNNs as a potent tool in NLP and suggests future research directions to enhance their applicability and effectiveness further.
SN  - 3068-6652
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{elikten2026Exploring,
  author = {Tuğba Çelikten and Aytuğ Onan},
  title = {Exploring Graph-Based Techniques in Text Data Processing: A Comprehensive Survey of NLP Advancements},
  journal = {ICCK Transactions on Emerging Topics in Artificial Intelligence},
  year = {2026},
  volume = {3},
  number = {2},
  pages = {86-127},
  doi = {10.62762/TETAI.2025.740330},
  url = {https://www.icck.org/article/abs/TETAI.2025.740330},
  abstract = {Graph Neural Networks (GNNs) have become increasingly prominent in Natural Language Processing (NLP) due to their ability to model intricate relationships and contextual connections between texts. Unlike traditional NLP methods, which typically process text linearly, GNNs utilize graph structures to represent the complex relationships between texts more effectively. This capability has led to significant advancements in various NLP applications, such as social media interaction analysis, sentiment analysis, text classification, and information extraction. Notably, GNNs excel in scenarios with limited labeled data, often outperforming traditional approaches by providing deeper, context-aware solutions. Their versatility in handling different data types has made GNNs a popular choice in NLP research. In this study, we thoroughly explored the application of GNNs across various NLP tasks, demonstrating their advantages in understanding and representing text relationships. We also examined how GNNs address traditional NLP challenges, showcasing their potential to deliver more meaningful and accurate results. Our research underscores the value of GNNs as a potent tool in NLP and suggests future research directions to enhance their applicability and effectiveness further.},
  keywords = {graph neural networks (GNN), natural language processing (NLP), graph convolutional networks (GCN), heterogeneous graph neural networks, GNN-based NLP},
  issn = {3068-6652},
  publisher = {Institute of Central Computation and Knowledge}
}
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CC BY Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
ICCK Transactions on Emerging Topics in Artificial Intelligence

ICCK Transactions on Emerging Topics in Artificial Intelligence

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