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Text Classification using Graph Mining-based Feature Extraction [chapter]

Chuntao Jiang, Frans Coenen, Robert Sanderson, Michele Zito
2009 Research and Development in Intelligent Systems XXVI  
A graph-based approach to document classification is described in this paper.  ...  Weighted subgraph mining is used to ensure classification effectiveness and computational efficiency; only the most significant subgraphs are extracted.  ...  Conclusion An approach to text classification using a graph based representation has been described.  ... 
doi:10.1007/978-1-84882-983-1_2 dblp:conf/sgai/JiangCSZ09 fatcat:vhc3wdnvgzbglexbhib6pewy7i

Text classification using graph mining-based feature extraction

Chuntao Jiang, Frans Coenen, Robert Sanderson, Michele Zito
2010 Knowledge-Based Systems  
A graph-based approach to document classification is described in this paper.  ...  Weighted subgraph mining is used to ensure classification effectiveness and computational efficiency; only the most significant subgraphs are extracted.  ...  Conclusion An approach to text classification using a graph based representation has been described.  ... 
doi:10.1016/j.knosys.2009.11.010 fatcat:6yle6yjdg5hl5g7ezbczzyiuy4

Features Analysis of Online Shopping System Using WCM

Maria Erum, Muhammad Waqas, Sidra Arshad, Tahir Nawaz
2018 EAI Endorsed Transactions on Scalable Information Systems  
Data mining techniques being used for web information extraction are unbelievable systems and suggested for the protection of extremely susceptible data.  ...  By the web sources huge amount of data is maintained and can be easily retrieved by using the web mining techniques as the techniques are applied exactly based on the needs of the users.  ...  Feature selection First and most important process is pre-processing in classification and pattern recognition in data mining are Feature extraction or selection.  ... 
doi:10.4108/eai.13-4-2018.154471 fatcat:gv5446uooba2fcmeuawgpaykoa

Text Categorization as a Graph Classification Problem

Francois Rousseau, Emmanouil Kiagias, Michalis Vazirgiannis
2015 Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)  
In this paper, we consider the task of text categorization as a graph classification problem.  ...  mining.  ...  Unsupervised feature mining using gSpan We considered the task of text categorization as a graph classification problem by representing textual documents as graph-of-words and then extracting subgraph  ... 
doi:10.3115/v1/p15-1164 dblp:conf/acl/RousseauKV15 fatcat:mqpatc2vfngxfczuzvxg3u5ud4

Movie Review Summarization Using Supervised Learning and Graph-Based Ranking Algorithm

Atif Khan, Muhammad Adnan Gul, Mahdi Zareei, R. R. Biswal, Asim Zeb, Muhammad Naeem, Yousaf Saeed, Naomie Salim
2020 Computational Intelligence and Neuroscience  
Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary.  ...  This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector.  ...  Feature Extraction. e aim of this phase is to extract features for review classification by employing a well-known feature extraction technique called bag of words (BoW).  ... 
doi:10.1155/2020/7526580 pmid:32565772 pmcid:PMC7288188 fatcat:slaaxbwbdbbbnl76u7yntaj3hu

Data mining in software engineering

M. Halkidi, D. Spinellis, G. Tsatsaronis, M. Vazirgiannis
2011 Intelligent Data Analysis  
Based on this classification we survey the mining approaches that have been used and categorize them according to the corresponding parts of the development process and the task they assist.  ...  The increased availability of data created as part of the software development process allows us to apply novel analysis techniques on the data and use the results to guide the process's optimization.  ...  The proposed classification model consists of three steps: -define the training dataset extracting features from behavior graphs -learn an SVM classifier using these features -classify new behavior graphs  ... 
doi:10.3233/ida-2010-0475 fatcat:xicaswfegfemtkplnngcfmd23q

Text Complexity Classification Data Mining Model Based on Dynamic Quantitative Relationship between Modality and English Context

Dan Zhang, Gengxin Sun
2021 Mathematical Problems in Engineering  
on text mining technology is proposed, which is embodied in the use of web crawler technology.  ...  complexity acquisition, recognition, and expression, to obtain a text complexity analysis based on text mining technology.  ...  Choi, “Text classification using verb MUST.  ... 
doi:10.1155/2021/4805537 fatcat:zfnleith7vcwjbkokxkcfprhaq

A Feature-Enhanced Ranking-Based Classifier for Multimodal Data and Heterogeneous Information Networks

Scott Deeann Chen, Ying-Yu Chen, Jiawei Han, Pierre Moulin
2013 2013 IEEE 13th International Conference on Data Mining  
We propose a heterogeneous information network mining algorithm: feature-enhanced RankClass (F-RankClass).  ...  Only text and text features are used to build networks.  ...  Unigram text features [12] are extracted from both the main text and the captions of images. Dense SIFT Bag-of-features image features [13] are extracted from images.  ... 
doi:10.1109/icdm.2013.71 dblp:conf/icdm/ChenCHM13 fatcat:kdi6qxevufaivb3dq2o2dmjhjq

Semantic data mining: A survey of ontology-based approaches

Dejing Dou, Hao Wang, Haishan Liu
2015 Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)  
The formal structure of ontology makes it a nature way to encode domain knowledge for the data mining use. In this survey paper, we introduce general concepts of semantic data mining.  ...  We provide detail discussions for the advances and state of art of ontology-based approaches and an introduction of approaches that are based on other form of knowledge representations.  ...  Classification task without labeled or annotated data is also reported in the ontology-based text classification task [7] . V.  ... 
doi:10.1109/icosc.2015.7050814 dblp:conf/semco/DouWL15 fatcat:uck4bd3gpnf5bgb4jike6xm5ne

Relational Graph Convolutional Network for Text-Mining-Based Accident Causal Classification

Zaili Chen, Kai Huang, Li Wu, Zhenyu Zhong, Zeyu Jiao
2022 Applied Sciences  
To address the aforementioned problems, this study proposes a text-mining-based accident causal classification method based on a relational graph convolutional network (R-GCN) and pre-trained BERT.  ...  However, the existing methods either rely on large corpus and data preprocessing methods, which are cumbersome, or extract text information based on bidirectional encoder representation from transformers  ...  To address the aforementioned problems, this study proposes a text-mining-based accident causal classification method based on a relational graph convolutional network (R-GCN) and pre-trained BERT.  ... 
doi:10.3390/app12052482 fatcat:rl664t7xl5chjihbgvd4g5hmbi

Two Methodologies Applied to the Author Profiling Task

Yuridiana Alemán, Nahun Loya, Darnes Vilariño Ayala, David Pinto
2013 Conference and Labs of the Evaluation Forum  
The obtained results were quite positive for the first methodology which considers a classicaly approach of classification, using diverse features extracted from the texts in order to feed a classifier  ...  The second methodology, based on graph mining techniques, obtained a very poor performance for the author profiling task.  ...  Afterwards, the texts are represented by means of graphs which will be further used for extracting relevant features. Graphs are mined using the SUBDUE tool 2 .  ... 
dblp:conf/clef/AlemanLAP13 fatcat:s4cetus7ifdldctrqf3cc3tiey

A Survey of Sentiment Analysis for Journal Citation

G. Parthasarathy, D. C. Tomar
2015 Indian Journal of Science and Technology  
In this paper we recommend different techniques available for high accuracy extraction of citations for academic papers and improve the performance in citation extraction by integration of two techniques  ...  Due to wide availability of data, there is an upcoming need for turning such an overwhelming amount of data into useful knowledge.  ...  The list of feature extraction techniques are given in Table 2 . The n-gram is of use in features for feature mining. It is a contiguous chain of n objects from a specified chain of text.  ... 
doi:10.17485/ijst/2015/v8i35/55134 fatcat:jlw5jx5mpndrnc4z7ky2hxmx2y

Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Atif Khan, Muhammad Adnan Gul, M. Irfan Uddin, Syed Atif Ali Shah, Shafiq Ahmad, Muhammad Dzulqarnain Al Firdausi, Mazen Zaindin
2020 Scientific Programming  
Different text features are used to calculate the salience score of each review sentence in clusters.  ...  For movie review classification, bag-of-words feature extraction technique is used to extract unigrams, bigrams, and trigrams as a feature set from given review documents, and represent the review documents  ...  accomplish the aim of sentiment classification/opinion mining by extracting and selecting an appropriate set of features.  ... 
doi:10.1155/2020/5812715 fatcat:luspae5ycndp3bmxx25td5mvya

A Review on Text Mining in Data Mining

Yogapreethi N, Maheswari S
2016 International Journal of Soft Computing  
Text mining extracts the quality information highly from text. Statistical pattern learning is used to high quality information.  ...  This survey is about the various techniques and algorithms used in text mining.  ...  Relevance feature discovery based on both positive and negative feedback for text mining models.  ... 
doi:10.5121/ijsc.2016.7301 fatcat:5g7upbyl2vgv3e5ee54ayb46ju

Rumour Detection based on Graph Convolutional Neural Net

Na Bai, Fanrong Meng, Xiaobin Rui, Zhixiao Wang
2021 IEEE Access  
For the rumor detection task, structural information in a conversation can be used to extract effective features.  ...  To make full use of global structural features and content information, we propose Source-Replies relation Graph (SR-graph) for each conversation, in which every node denotes a tweet, its node feature  ...  The proposed EGCN use both text features and structural features for classification, and the experiments verify that the extracted text features and structural features are effective for the rumor detection  ... 
doi:10.1109/access.2021.3050563 fatcat:7g2bmqf7uff3znbmsywudzb3r4
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