A Survey on the Multiple Classifier for New Benchmark Dataset of Vietnamese News Classification

Duong, Huu-Thanh; Hoang, Vinh Truong

doi:10.1109/kst.2019.8687509

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…Based on the experiments [24], we choose the best classifiers for our experiments, namely logistic regression, SVM and ensembles of classifiers as OVO and OVR.…”

Section: Classifiersmentioning

confidence: 99%

A review: preprocessing techniques and data augmentation for sentiment analysis

Duong

Nguyen-Thi

2021

Comput Soc Netw

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In literature, the machine learning-based studies of sentiment analysis are usually supervised learning which must have pre-labeled datasets to be large enough in certain domains. Obviously, this task is tedious, expensive and time-consuming to build, and hard to handle unseen data. This paper has approached semi-supervised learning for Vietnamese sentiment analysis which has limited datasets. We have summarized many preprocessing techniques which were performed to clean and normalize data, negation handling, intensification handling to improve the performances. Moreover, data augmentation techniques, which generate new data from the original data to enrich training data without user intervention, have also been presented. In experiments, we have performed various aspects and obtained competitive results which may motivate the next propositions.

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“…Based on the experiments [24], we choose the best classifiers for our experiments, namely logistic regression, SVM and ensembles of classifiers as OVO and OVR.…”

Section: Classifiersmentioning

confidence: 99%

A review: preprocessing techniques and data augmentation for sentiment analysis

Duong

Nguyen-Thi

2021

Comput Soc Netw

Self Cite

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“…This model establishes the best-fit linear line between independent and dependent variables, quantifying the linear relationship between them. Notable for ease of implementation, interpretation, and efficiency in training, linear regression excels in handling linearly separable data [15]. However, it is susceptible to noise and overfitting, and its ability to ascertain relationships does not guarantee knowledge of underlying causal mechanisms.…”

Section: Linear Regression (Lr)mentioning

confidence: 99%

Advancements in News Article Classification: Approaches in Machine Learning and Deep Learning across Sports, Entertainment, Politics, Business, and Weather Domains

Ramzan,

et al. 2023

VAWKUM trans. comput. sci.

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The classification of news articles is a crucial technology for processing news information, aiding in the organization of information. It is challenging to classify news due to the continuous emergence of news that requires processing. The modern technological era has reshaped traditional lifestyles in various domains. Similarly, the medium of publishing news and events has experienced rapid growth with the advancement of Information Technology. In this research, news article classification is organized into five selected domains: sports, entertainment, politics, business, and weather news. The classification involves both common and uncommon approaches, along with datasets based on Machine Learning and Deep Learning techniques. Furthermore, the evaluation incorporates various metrics such as precision, recall, and accuracy to compare approaches across the selected five news domains with datasets. To narrow the focus, we limited the news categorization to a few domains (sports, entertainment, politics, business, and weather) to facilitate a better understanding of a large amount of data through concise content. We recommend our work to individuals interested in extending and building upon my research over time.

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“…Previous works [36][37][38][39] showed a comparative study of classifiers to evaluate the performance of NLP tasks. So, we apply three well-known classifiers such as logistics regression, Naïve Bayes, and random forest.…”

Section: Classifiersmentioning

confidence: 99%

A Computational Linguistic Approach for Gender Prediction Based on Vietnamese Names

Huong

Tran-Trung

Hoang

2022

Mobile Information Systems

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Gender prediction is extensively studied in recent years since it is widely applied in many fields. Several factors have been investigated to determine a gender of male or female through facial images, voice, gait, finger print, etc. In this study, we present a machine learning approach for gender determination based on Vietnamese names. A model based on N-gram for the full name, combining its own middle name feature based on the specificity of Vietnamese language, is proposed. The experimental evaluation of gender prediction tasks is applied on GenderVN1.0 dataset (with 3 million Vietnamese names) that achieves 90.9% of accuracy.

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A Survey on the Multiple Classifier for New Benchmark Dataset of Vietnamese News Classification

Cited by 12 publications

References 8 publications

A review: preprocessing techniques and data augmentation for sentiment analysis

A review: preprocessing techniques and data augmentation for sentiment analysis

Advancements in News Article Classification: Approaches in Machine Learning and Deep Learning across Sports, Entertainment, Politics, Business, and Weather Domains

A Computational Linguistic Approach for Gender Prediction Based on Vietnamese Names

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