News Text Classification Research Articles

NAKNN: An Efficient Classification of Indonesian News Texts with Nazief-Adriani and KNN

Internet usage in Indonesia has seen a significant increase, reaching 215.63 million users in 2022-2023, or 78.19% of the population. With the ease of internet access, digital news portals like Narasi TV have become a primary source of information for many people. However, the large number of news articles makes manual categorizing challenging. This study aims to classify Indonesian-language news documents from Narasi TV using the Nazief-Adriani algorithm for stemming and the K-Nearest Neighbor (KNN) method for classification. The text mining process begins with preprocessing, which includes case folding, tokenizing, stop-word filtering, and stemming. Using a dataset of 500 news documents, the study demonstrated that with a 90:10 data split, the average accuracy reached 93%, with the highest value being 100%. For the 80:20 data split, the average accuracy was 89%, with the highest value being 93%, and for a 70:30 data split, the average accuracy was 87%, with the highest value being 89%. In conclusion, the combination of the Nazief-Adriani algorithm and the KNN method with optimal k selection and random states obtained high accuracy, obtaining an average accuracy of 93%) in classifying Indonesian-language news documents. These results demonstrate the significant potential of text mining and classification techniques to manage digital news.

Research of news text classification method based on hierarchical semantics and prior correction

News text is an important branch of natural language processing. Compared to ordinary texts, news text has significant economic and scientific value. The characteristics of news text include structural hierarchy, diverse label categories, and limited high-quality annotation samples. Many machine learning and deep learning methods exist to analyze various forms of news text. However, due to label imbalance, hierarchical semantics, and confusing labels, current methods have limitations. Therefore, this paper proposes a news text classification framework based on hierarchical semantics and prior correction (HSPC). Firstly, data augmentation is used to enhance the diversity of the training set and adversarial learning is employed to improve the resistance of the model with its robustness. Then, a hierarchical feature extraction approach is employed to extract semantic features from different levels of news texts. Consequentially, a feature fusion method is designed to allow the model to focus on relevant hierarchical semantics for label classification. Finally, highly confusing label predictions are corrected to optimize the label prediction of the model and improve confidence. Multiple experiments are performed on four widely used public datasets. The experimental results indicate that HSPC achieves higher classification accuracy compared to other models. On the FCT, AGNews, THUCNews, and Ohsumed datasets, HSPC improves the accuracy by 1.03%, 1.38%, 2.55%, and 1.15%, respectively, compared to state-of-the-art methods. This validates the rationality and effectiveness of the designed mechanisms.

Text data augmentation and pre-trained Language Model for enhancing text classification of low-resource languages.

In the domain of natural language processing (NLP), the development and success of advanced language models are predominantly anchored in the richness of available linguistic resources. Languages such as Azerbaijani, which is classified as a low-resource, often face challenges arising from limited labeled datasets, consequently hindering effective model training. The primary objective of this study was to enhance the effectiveness and generalization capabilities of news text classification models using text augmentation techniques. In this study, we solve the problem of working with low-resource languages using translations using the Facebook mBart50 model, as well as the Google Translate API and a combination of mBart50 and Google Translate thus expanding the capabilities when working with text. The experimental outcomes reveal a promising uptick in classification performance when models are trained on the augmented dataset compared with their counterparts using the original data. This investigation underscores the immense potential of combined data augmentation strategies to bolster the NLP capabilities of underrepresented languages. As a result of our research, we have published our labeled text classification dataset and pre-trained RoBERTa model for the Azerbaijani language.

Retracted: News Text Classification Method Based on the GRU_CNN Model

Retracted: News Text Classification Method Based on the GRU_CNN Model

American literature news narration based on computer web technology.

Driven by internet technology, online has become the main way of news dissemination, but redundant information such as navigation bars and advertisements affects people's access to news content. The research aims to enable users to obtain pure news content from redundant web information. Firstly, based on the narrative characteristics of literary news, the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm is employed to extract pure news content from the analyzed web pages. The algorithm uses keyword matching, text analysis, and semantic processing to determine news content's boundaries and key information. Secondly, the news text classification algorithm (support vector machine, K-nearest neighbor, AdaBoost algorithm) is selected through comparative experiments. The news extraction system based on keyword feature and extended Document Object Model (DOM) tree is constructed. DOM technology analyzes web page structure and extracts key elements and information. Finally, the research can get their narrative characteristics by studying the narrative sequence and structure of 15 American literary news reports. The results reveal that the most used narrative sequence in American literary news is sequence and flashback. The narrative duration is dominated by the victory rate and outline, supplemented by scenes and pauses. In addition, 53.3% of the narrative structures used in literary news are time-connected. This narrative structure can help reporters have a clear conceptual structure when writing, help readers quickly grasp and understand the context of the event and the life course of the protagonists in the report, and increase the report's readability. This research on the narrative characteristics of American literature news can provide media practitioners with a reference on news narrative techniques and strategies.

Retracted: News Text Classification Method and Simulation Based on the Hybrid Deep Learning Model

Retracted: News Text Classification Method and Simulation Based on the Hybrid Deep Learning Model

Classification of News Texts from Different Languages with Machine Learning Algorithms

As a result of the developments in technology, the internet is accepted as one of the most important sources of information today. Although it is possible to access a large number of data in a short time thanks to the Internet, it is critical to analyze this data correctly. The need for text mining is increasing day by day by processing and analyzing the increasingly irregular text type data in the digital environment and classifying them in a meaningful way. In this study, news texts obtained from online German, Spanish, English and Turkish news sites were separated according to predetermined world, sports, economy and politics categories. The data set consisting of 4000 news texts was classified using 41 different machine learning algorithms in the Weka program. The highest successful classification was obtained with Naive Bayes Multinominal and Naive Bayes Multinominal Updateable algorithms, and 93.5% for German news texts, 93.3% for English news texts, 82.8% for Spanish news texts and 88.8% for Turkish news texts.

News text classification using Long-Term Short Memory (LSTM) algorithm

Over the past few years, the classification of texts has become increasingly important. Because knowledge is now available to users through various sources namely electronic media, digital media, print media, and many more. One of them is the development of so much news every day. LSTM is one of the algorithms of deep learning methods that can classify a text. This research proves for the LSTM algorithm on the classification of news text sentences. The data used is the news text from the Kaggle data center set i.e. aggregator news data. The results of the LSTM experiment from 10 epochs obtained with an accuracy value of 93,15% on the classification of texts into four categories, namely entertainment, bussines, science, and health.

Classification of News Texts with GloVe Word Embeddings and Neural Networks

Dijital haberlerin artan miktarları, istenilen türdeki haberlere doğru ve hızlı bir şekilde erişim için haber metinlerinin kategorilere ayrılmasını gerektirmektedir. Bu çalışmada, ön-eğitimli kelime gömülmelerinin, Uzun Ömürlü Kısa Dönem Bellek Ağı (Long-Short Term Memory, LSTM) ve Evrişimsel Sinir Ağları (Convolutional Neural Network, CNN) gibi derin öğrenme modelleri üzerindeki etkisi araştırılmaktadır. Global Vektör (GloVe) kelime gömülmelerinden alınan bağlamsal temsilleri girdi olarak alan LSTM ve CNN ağları kullanılarak haber metinleri sınıflandırılmıştır. Kapsamlı ve karşılaştırmalı araştırmaların eksikliği nedeniyle GloVe gömme katmanı tarafından sağlanan bağlamsal temsiller farklı sınıflandırıcılar ve veri setleri üzerinde test edilmektedir. Deneysel süreçler boyunca Türkçe Haber başlıklarından oluşan Turkish Headlines veri seti ve BBC News Classification veri setleri kullanılmıştır. Kelime gömülmelerinin ağlar üzerindeki etkisini ortaya koymak için deneysel süreçler aynı parametreler ile tekrarlanmıştır. LSTM modelinde Glove kelime gömülme yöntemi kullanıldığında modelin başarısının %81’den %91’e çıktığı gözlemlenmektedir. CNN modelinde ise Glove kelime gömülmelerinin modelin başarısının olumlu yansımadığı görülmektedir.

Chinese News Text Classification Method via Key Feature Enhancement

(1) Background: Chinese news text is a popular form of media communication, which can be seen everywhere in China. Chinese news text classification is an important direction in natural language processing (NLP). How to use high-quality text classification technology to help humans to efficiently organize and manage the massive amount of web news is an urgent problem to be solved. It is noted that the existing deep learning methods rely on a large-scale tagged corpus for news text classification tasks and this model is poorly interpretable because the size is large. (2) Methods: To solve the above problems, this paper proposes a Chinese news text classification method based on key feature enhancement named KFE-CNN. It can effectively expand the semantic information of key features to enhance sample data and then combine the zero–one binary vector representation to transform text features into binary vectors and input them into CNN model for training and implementation, thus improving the interpretability of the model and effectively compressing the size of the model. (3) Results: The experimental results show that our method can significantly improve the overall performance of the model and the average accuracy and F1-score of the THUCNews subset of the public dataset reached 97.84% and 98%. (4) Conclusions: this fully proved the effectiveness of the KFE-CNN method for the Chinese news text classification task and it also fully demonstrates that key feature enhancement can improve classification performance.

Classification of News Texts by Categories Using Machine Learning Methods

In parallel with the advances in technology, digital journalism is preferred more than printed journalism day by day. Due to the fast and up-to-date sense of journalism provided by digital journalism and its ubiquitous accessibility features, it is read more by users. In addition to these advantages provided by digital journalism, it also has some difficulties compared to printed journalism. The stage of preparation and delivery of the news to the user requires more technological knowledge and equipment compared to printed journalism. The processes of title selection, text creation, photo selection and determination of the appropriate news category in the preparation phase of the news are designed to be both faster and user-friendly compared to printed publishing. The news created to be presented to the target audience may belong to one or more of different categories such as economy, politics, sports, technology, and health. The inclusion of the news in the appropriate category provides convenience in terms of reaching the right audience and archiving the news correctly. In this study, news texts were classified according to their categories based on the machine learning methods. In the study, news of five newspapers in three different categories were used. Bayesian classifier and decision tree methods were used to classify the news in the dataset including a total of 10.500 news. In the results of the study, it was observed that the Bayesian classifier classified the news more successfully according to their categories.

Research on news text classification based on RCNN

One of the popular directions of natural language processing is text processing, which is an effective method used to manage data, and it has developed rapidly after the rise of artificial intelligence. However, text classification based on traditional machine learning algorithms encountered bottlenecks in the development process, and after deep learning, a multi-level neural network, was introduced, it was soon widely used in natural language processing problems, making many branches of the field develop further, and now deep learning is the mainstream model in the field of text classification. In this paper, we first introduce the general process of text classification, point out the current problems of text classification based on traditional machine learning, and then describe the superiority of deep learning in this aspect of feature extraction. After that, three different deep learning models are experimented, and the differences between the trained models are compared and the relative optimal models are derived. Finally, the future trends and research directions of deep learning in this field are discussed.

Analysis and Comparison of Chinese News Text Classification Methods Based on Deep Learning

As people in today's world consume an increasing amount of information, the number of Internet News is also vastly increasing. Facing all sorts of different kinds of news, how to accurately distinguished different types of news becomes the direction of many scholars' study.This article uses word cloud to represent keywords used in different domains of news. Moreover, we used two methods: TF-IDF and TextRank, to identify and analyze keywords of different fields of news. To understand the performance using various classification methods, we choose the THUCNews data sets. This data set collects ten fields of news in the history of Weibo. Moreover, we choose nine different kinds of machine learning algorithms, including SVM, XGBoost, RandomForest, GBDT, GRU, LSTM, CNN, RNN, and MLP, to investigate their performance. Among these nine models, GRU has an accuracy of 96.93%, SVM has an accuracy of 96.39%, CNN has an accuracy of 94.72%, and RandomForest has an accuracy of 92.97%, which make them stand out in their similar models. We used word-embedding vectorization for the Neural Network algorithm and TF-IDF vectorization for the others.

(Retracted) News image text classification algorithm with bidirectional encoder representations from transformers model

With the development of Internet technology and the transformation of news media to informatization, news images, texts, and other information have exploded. The news image and text classification can effectively solve the disorder problem of news information. The early news image text classification is to establish artificial classifiers according to specific classification rules, but the classification error rate is high and the classification speed is slow. Later, machine learning technology replaced manual classification of news image texts. Although the classification efficiency has been greatly improved, the classification time is still a bit long. The bidirectional encoder representations from transformers (BERT) model uses transformer and encoder to pretrain news image text to improve classification efficiency. By comparing the differences between machine learning and BERT models in news image text classification, the experiments showed that the average precision, recall, and F1 values of the news image text classification algorithm using the BERT model were 96.6%, 95.7%, and 96.1%, respectively. All three evaluation criteria were about 5% more than the classification algorithm of the machine learning model. The classification speed of the news image text classification algorithm using the BERT model was 1.8 times that of the news image text classification algorithm based on support vector machine. Therefore, the news image text classification algorithm using the BERT model can improve the classification accuracy and efficiency of news image text.

News Text Classification Method Based on the GRU_CNN Model

The convolutional neural network can extract local features of text but cannot capture structure information or semantic relationships between words, and a single CNN model’s classification performance is low, whereas GRU can effectively extract semantic information and global structure relationships of text. To address this problem, this paper proposes a news text classification method based on the GRU_CNN model, which combines the advantages of CNN and GRU. The model first trains word vectors as the embedding layer with the Word2vec model and then extracts semantic information from text sentences with the GRU model. Following that, this model employs the CNN model to extract crucial semantic information features and finally completes the classification through the Softmax layer. The experimental results reveal that the GRU_CNN hybrid model outperforms single CNN, LSTM, and GRU models in terms of classification effect and accuracy.

WTL-CNN: a news text classification method of convolutional neural network based on weighted word embedding

The word embedding model word2vec tends to ignore the importance of a single word to the entire document, which affects the accuracy of the news text classification method. To address this problem, a method that combines word2vec, a topic-based TF-IDF algorithm, and an improved convolutional neural network is proposed in this paper, which is named WTL-CNN. Firstly, word2vec is used to convert text data into word vectors. Secondly, an improved TF-IDF algorithm is proposed to weight word vectors. The improved TF-IDF algorithm introduces LDA topic generation model to enhance the topic semantic information of TF-IDF values. Thirdly, according to the location distribution law of important features of news texts, the location information is converted into weights and integrated into the pooling process convolutional neural network to further improve the accuracy of classification. At last, WTL-CNN has been evaluated and compared with seven contrast models on datasets THUCNews and SogouCS under the environment of TensorFlow. The experimental results show that the precision rate, recall rate and F1 value of WTL-CNN model reach 95.76%, 93.43%, 94.98% respectively on the THUCNews, and reach 94.61%, 93.43%, 94.01 respectively on the SogouCS.

Chinese News Text Classification and Its Application Based on Combined-Convolutional Neural Network

<p>A method based on combined-convolutional neural network (Combined-CNN) for Chinese news text classification is proposed. First of all, in order to solve the problem of a lack of special term set for Chi-nese news classification, a vocabulary suitable for Chinese long text classification is made by construct-ing a data index method. The Word2Vec pre-trained model was used to embed the text features word vectors. Second, by optimizing the structure of the classical convolutional neural network (CNN) model, a new idea of Combined-CNN model is proposed, which solves the problem of incomplete feature ex-traction of local text blocks and improves the accuracy rate of Chinese news text classification. Effective model regularization and RAdam optimization algorithm are designed in the model to enhance the model training effect. The experimental results show that the precision of the Combined-CNN model for Chi-nese news text classification reaches 93.69%. Compared with traditional machine learning methods and deep learning algorithms, the accuracy rate is improved by a maximum of 11.82% and 1.9%, respectively, and it is better than the comparison model in Recall and F-Measure. Finally, the Chinese news classifica-tion algorithm of the Combined-CNN is applied to realize a personalized recommendation system.</p> <p> </p>

Urdu News Content Classification Using Machine Learning Algorithms

As the world has become a global village, the flow of news in terms of volume and speed increases. It is necessary to engage computing machines for assisting people in dealing with this massive data. The availability of different types of news and such material on the Internet serves as a source of information for billions of users. Millions of people in our subcontinent speak and understand Urdu. There are several classification techniques that are available and are applied to classify English news like political, Education, Medical, etc. Plenty of research work has been done in multiple languages but Urdu is still to be worked on due to a lack of resources. This research evaluates the performance of twelve (12) different Machine learning classifiers for the Urdu News text Classification problem. The analysis was performed on a relatively big and recent collection of Urdu text that contains over 0.15 million (153,050) labeled instances of eight different classes. In addition, after applying pre-processing techniques, the TF-IDF weighting technique was adopted for feature selection and data extraction. After evaluating various machine learning methods, the SVM outperforms the other eleven algorithms with an accuracy of 91.37 %. We also compare its results with other classifiers like linear SVM, Logistic regression, SGD, Naïve bays, ridge regression, and a few others.

Chinese News Text Classification Based on Convolutional Neural Network

With the explosive growth of Internet text information, the task of text classification is more important. As a part of text classification, Chinese news text classification also plays an important role. In public security work, public opinion news classification is an important topic. Effective and accurate classification of public opinion news is a necessary prerequisite for relevant departments to grasp the situation of public opinion and control the trend of public opinion in time. This paper introduces a combined-convolutional neural network text classification model based on word2vec and improved TF-IDF: firstly, the word vector is trained through word2vec model, then the weight of each word is calculated by using the improved TF-IDF algorithm based on class frequency variance, and the word vector and weight are combined to construct the text vector representation. Finally, the combined-convolutional neural network is used to train and test the Thucnews data set. The results show that the classification effect of this model is better than the traditional Text-RNN model, the traditional Text-CNN model and word2vec-CNN model. The test accuracy is 97.56%, the accuracy rate is 97%, the recall rate is 97%, and the F1-score is 97%.

Research on Topic Fusion Graph Convolution Network News Text Classification Algorithm

Abstract In the face of a large amount of news text information, how to make a reasonable classification of news text is a hot issue of modern scholars. To solve the problem that only word co-occurrence was considered in the Text Graph Convolutional Network (Text-GCN) method to build a graph model, a news text classification algorithm which fuses themes and is based on Graph Convolution Network, is presented. Firstly, the LDA topic model is used to process the corpus to obtain the distribution of themes of the corpus. Secondly, a graph model is built to construct a global map by using the related topic words and their subject distribution in each article. Finally, the text graph is input into the Graph Convolution Network layers to compute the learning representation of combining feature in order to complete the text classification task. The experimental results show that this method can effectively realize the word level interaction of information in text. In the experiment on Chinese and English datasets, adding theme information improves the accuracy by 1% compared with the Text-GCN method.