A quantitative analysis of the temporal effects on automatic text classification

Salles, Thiago; Rocha, Leonardo; Gonçalves, Marcos André; Almeida, Jussara M.; Mourão, Fernando; Meira, Wagner; Viegas, Felipe

doi:10.1002/asi.23452

Cited by 9 publications

(3 citation statements)

References 43 publications

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“…The main evaluation measures applied in this study are the F1 measure, and rand index (RI). F1 measure is the harmonic mean between precision and recall, and is also widely used to measure clustering [ 45 , 46 ], which is calculated in the standard approach, i.e., Eq ( 12 ). Precision and recall are widely used in classification and clustering tasks for measuring the relevance [ 47 ], and are defined as Eqs ( 13 ) and ( 14 ) respectively.…”

Section: Dataset and Experimental Resultsmentioning

confidence: 99%

Hybrid self-optimized clustering model based on citation links and textual features to detect research topics

Wang

Zhang

et al. 2017

PLoS ONE

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The challenge of detecting research topics in a specific research field has attracted attention from researchers in the bibliometrics community. In this study, to solve two problems of clustering papers, i.e., the influence of different distributions of citation links and involved textual features on similarity computation, the authors propose a hybrid self-optimized clustering model to detect research topics by extending the hybrid clustering model to identify “core documents”. First, the Amsler network, consisting of bibliographic coupling and co-citation links, is created to calculate the citation-based similarity based on the cosine angle of papers. Second, the cosine similarity is also used to compute the text-based similarity, which consists of the textual statistical and topological features. Then, the cosine angle of the linear combination of citation- and text-based similarity is considered as the hybrid similarity. Finally, the Louvain method is applied to cluster papers, and the terms based on term frequency are used to label clusters. To test the performance of the proposed model, a dataset related to the data envelopment analysis field is used for comparison and analysis of clustering results. Based on the benchmark built, different clustering methods with different citation links or textual features are compared according to evaluation measures. The results show that the proposed model can obtain reasonable and effective clustering results, and the research topics of data envelopment analysis field are also analyzed based on the proposed model. As different features are considered in the proposed model compared with previous hybrid clustering models, the proposed clustering model can provide inspiration for further studies on topic identification by other researchers.

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Section: Dataset and Experimental Resultsmentioning

confidence: 99%

Hybrid self-optimized clustering model based on citation links and textual features to detect research topics

Wang

Zhang

et al. 2017

PLoS ONE

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“…With large‐scale, constantly increasing ecologically relevant information, it is important to rapidly comprehend and classify the new diversifications of wetland documents. Aiming to alleviate the human labor costs for analyzing large‐scale sets of wetland documents, the technology of automatic text modeling has emerged to deal with these issues (Salles et al, ). The methods consist automatic allocation of a text document to a set of predefined categories using machine learning tricks.…”

Section: Introductionmentioning

confidence: 99%

Comprehending international important Ramsar wetland documents using latent semantic topic model in kernel space

Lin

Jiang

et al. 2019

Natural Resource Modeling

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The kernel‐based statistical semantic topic model is introduced for comprehending three species of internationally important Ramsar wetland documents describing the Lashi Lake wetland in the Yunnan Province, the Yancheng wetland in the Jiangsu Province, and the Zoige wetland in the Sichuan Province of China. Latent Dirichlet allocation (LDA) features are used to represent the semantic components of wetland documents. Kernel principal component analysis (KPCA) maps the topic components to the kernel space to attain the low dimensional principal components. Support vector machines (SVMs) are used to comprehend the semantic distribution of distinct wetland documents in the kernel space. The LDA+KPCA+SVM algorithm reaches 77.0% training and 75.9% test accuracy and 0.902 training and 0.840 test mean average precision scores in the application of comprehending the wetland documents, respectively. The performance of the proposed kernel‐based model is superior to the traditional models of LDA+SVM and LDA+PCA+SVM.

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“…Today's large‐scale text data, our case study in this research, are also an important high‐dimensional application field where the space regularly contains at least several thousands of distinct terms and is very sparse and noisy. We focus on text classification, which is a useful subdiscipline of data mining and is an active research path nowadays . Given a set of text documents with known class labels, text classification intends to predict the class of new instances.…”

Section: Introductionmentioning

confidence: 99%

Estimator learning automata for feature subset selection in high‐dimensional spaces, case study: Email spam detection

Seyyedi

Minaei-Bidgoli

2018

Int J Communication

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One of the difficult challenges facing data miners is that algorithm performance degrades if the feature space contains redundant or irrelevant features. Therefore, as a critical preprocess task, dimension reduction is used to build a smaller space containing valuable features. There are 2 different approaches for dimension reduction: feature extraction and feature selection, which itself is divided into wrapper and filter approaches. In high-dimensional spaces, feature extrac-

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A quantitative analysis of the temporal effects on automatic text classification

Cited by 9 publications

References 43 publications

Hybrid self-optimized clustering model based on citation links and textual features to detect research topics

Hybrid self-optimized clustering model based on citation links and textual features to detect research topics

Comprehending international important Ramsar wetland documents using latent semantic topic model in kernel space

Estimator learning automata for feature subset selection in high‐dimensional spaces, case study: Email spam detection

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