Chinese News Text Classification Based on Machine Learning Algorithm

Miao, Fang; Zhang, Pu; Jin, Libiao; Wu, Hongda

doi:10.1109/ihmsc.2018.10117

Cited by 38 publications

(16 citation statements)

References 11 publications

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“…Finally applying performance evaluation metrics, they found that RR and PA exhibited highest accuracy of 99.96% using n-gram features. Authors Miao et al [12] in 2020 studied on Chinese news text adopted from Fudan University. To classify texts into nine categories this work analyzed the dataset through three classifiers (i.e.…”

Section: A Sentiment Analysis In English and Other Languagesmentioning

confidence: 99%

Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

2021

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We can state undoubtedly that Bangla language is rich enough to work with and implement various Natural Language Processing (NLP) tasks. Though it needs proper attention, hardly NLP field has been explored with it. In this age of digitalization, large amount of Bangla news contents are generated in online platforms. Some of the contents are inappropriate for the children or aged people. With the motivation to filter out news contents easily, the aim of this work is to perform document level sentiment analysis (SA) on Bangla online news. In this respect, the dataset is created by collecting news from online Bangla newspaper archive. Further, the documents are manually annotated into positive and negative classes. Composite process technique of “Pipeline” class including Count Vectorizer, transformer (TF-IDF) and machine learning (ML) classifiers are employed to extract features and to train the dataset. Six supervised ML classifiers (i.e. Multinomial Naive Bayes (MNB), K-Nearest Neighbor (K-NN), Random Forest (RF), (C4.5) Decision Tree (DT), Logistic Regression (LR) and Linear Support Vector Machine (LSVM)) are used to analyze the best classifier for the proposed model. There has been very few works on SA of Bangla news. So, this work is a small attempt to contribute in this field. This model showed remarkable efficiency through better results in both the validation process of percentage split method and 10-fold cross validation. Among all six classifiers, RF has outperformed others by 99% accuracy. Even though LSVM has shown lowest accuracy of 80%, it is also considered as good output. However, this work has also exhibited surpassing outcome for recent and critical Bangla news indicating proper feature extraction to build up the model.

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Section: A Sentiment Analysis In English and Other Languagesmentioning

confidence: 99%

Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

2021

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“…In general, machine learning based methods are more popular than knowledge engineering based methods, since the latter consume more time and are very inefficient. Recently, although methods based on machine learning are still very common [13], increasingly more classification tasks use neural network models such as the CNN and LSTM [4]- [13], [15], [39]. The basic process of text classification using a neural network model is to extract the information and features in the text through the deep neural network and then process the classification results by the classifier.…”

Section: Related Workmentioning

confidence: 99%

“…Chinese text classification methods in recent years have been based mainly on CNN and LSTM [6], [9], [10] as well as their variants and combinations, and they have achieved good results in the classification tasks of different languages [4], [11], [12]. Notably, although the machine learning algorithm has still been very popular in the last two years [13], some study results such as [39] show that deep neural networks have advantages over some machine learning methods in some classification tasks. Although [5] and [6] achieved good results using CNN-based methods, it is difficult for these methods to extract context features in the text due to the CNN structure itself.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Comprehensive Context Modeling for Chinese Text Classification

et al. 2019

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The Chinese text classification task is challenging compared to tasks based on other languages such as English due to the characteristics of the Chinese text itself. In recent years, some popular methods based on deep learning have been used for text classification, such as the convolutional neural network (CNN) and the long short-term memory (LSTM) network. However, some problems are still encountered when classifying Chinese text. For example, important but obscure context information in Chinese text is not easily extracted. To improve the effect of Chinese text classification, we propose a novel classification model in this paper named the hierarchical comprehensive context modeling network (HCCMN) that can extract more comprehensive context. Our approach aims to extract contextual information and integrate it with the original input and then extract hierarchically more context, spatial information and high-weight local features from the integrated results. In addition, our method can remember long-term historical obscure information. Since Chinese radiology texts are complicated and difficult to obtain, we collected a Chinese radiology medical text dataset (CIRTEXT) containing more than 56,000 real-world data samples to verify the effect of this work. We conducted experiments on four datasets and showed that our HCCMN performs at state-of-the-art levels on three selected evaluation metrics compared to baselines. We present promising results showing that our hierarchical context modeling network extracts useful context from Chinese text more effectively and comprehensively. INDEX TERMS Chinese text, classification, contextual information, deep neural network.

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“…[17] Sentiment Classification is successfully used in identification of customer feelings regarding of a product or a service. [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] Analyzing the customers feedback's and expectations is a major aid in measuring overall performance, sales and improving financial banking institutions marketing strategies, especially on their online presence. This paper introduces the research framework for a dataset creation in a financial banking domain in order to be further used in a Supervised Machine Learning context.…”

Section: Conclusion and Further Researchmentioning

confidence: 99%

Financial Banking Dataset for Supervised Machine Learning Classification

Raicu¹

2019

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Social media has opened new avenues and opportunities for financial banking institutions to improve the quality of their products and services and to understand and to adapt to their customers' needs. By directly analyzing the feedback of its customers, financial banking institutions can provide personalized products and services tailored to their customer needs. This paper presents a research framework for creation of a financial banking dataset in order to be used for Sentiment Classification using various Machine Learning methods and techniques. The dataset contains 2234 financial banking comments from Romanian financial banking social media collected via web scraping technique.

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Chinese News Text Classification Based on Machine Learning Algorithm

Cited by 38 publications

References 11 publications

Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

Hierarchical Comprehensive Context Modeling for Chinese Text Classification

Financial Banking Dataset for Supervised Machine Learning Classification

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