Classification Of News Articles Research Articles

TEXT CLASSIFICATION USING ADAPTIVE BOOSTING ALGORITHM WITH OPTIMIZATION OF PARAMETERS TUNING ON CABLE NEWS NETWORK (CNN) ARTICLES

The development of the era encourages advances in communication and information technology. This resulted in the exchange of information being faster because it is connected to the internet. One platform that provides online news articles is Cabel News Network (CNN), which has been broadcasting news on its website since 1995. The number of Cabel News Network news articles continues to increase, so news articles are categorized to make it easier for readers to find articles according to the category they want. Classification is a technique for determining the class of an object based on its characteristics, where the class label is known beforehand. One of the algorithms for classification is adaptive boosting (AdaBoost). The AdaBoost algorithm performs classification by building several weighted decision trees (stumps), then the class determination is based on the number of stumps with the highest weight. The AdaBoost algorithm can be combined with parameter tuning to avoid overfitting or underfitting resulting from a weak set of stumps. Therefore, this study implements the AdaBoost algorithm with parameter tuning on CNN news article classification. The data used in this study is CNN news article data from 2011 to 2022 sourced from the Kaggle page. The data is categorized into six classes, namely business, entertainment, health, news, politics, and sports. This study uses two evaluation metrics, namely the accuracy value and the confusion matrix to measure the performance of the AdaBoost algorithm. The accuracy value obtained is 0,78763, the precision value is 0.91, the recall value is 0.85, and the F1 score value is 0.88.

Correlation Between News and Stock Price Based on Stock Market Indices: Can News Classification Be Used as a Tool to Make Better Decisions?

The stock market is influenced by various factors, including news events, economic indicators, and investor sentiment. Understanding the correlation between news and stock price movements interests market participants and researchers. In this paper, we explore the relationship between news sentiment and stock market trends using stock market indices. We employ natural language processing (NLP) techniques to classify news articles and analyze their impact on stock market indices, focusing on the S&P 500 and the Dow Jones Industrial Average. We utilize sentiment analysis and machine learning algorithms, including Random Forest, Loughran-McDonald (2014) Financial Sentiment Word Lists (Extended), and AFINN Lexicon, to predict stock market trends based on news sentiment. Our findings demonstrate that positive news sentiment has a more significant impact on stock prices than negative sentiment. The Random Forest model achieves the highest accuracy, while domain-specific lexicons provide valuable insights into financial news sentiment. However, predicting negative trends remains a challenge across all methods. Our research contributes to the growing knowledge of the relationship between news and stock prices and provides valuable insights for market participants and researchers.

Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification.

Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient. Previous results demonstrated that these methods can even improve performance on some classification tasks. This paper complements existing research by investigating how these techniques influence classification performance and computation costs compared to full fine-tuning. We focus specifically on multilingual text classification tasks (genre, framing, and persuasion techniques detection; with different input lengths, number of predicted classes and classification difficulty), some of which have limited training data. In addition, we conduct in-depth analyses of their efficacy across different training scenarios (training on the original multilingual data; on the translations into English; and on a subset of English-only data) and different languages. Our findings provide valuable insights into the applicability of parameter-efficient fine-tuning techniques, particularly for multilabel classification and non-parallel multilingual tasks which are aimed at analysing input texts of varying length.

Fake News Detection Model

The proliferation of fake news across social media and various other platforms is a significant concern due to its potential to cause widespread social and national harm. Extensive research efforts are currently underway to identify and combat this issue. This study conducts an in-depth analysis of research pertaining to the detection of fake news, examining traditional machine learning models to determine the most effective approach. By leveraging supervised machine learning algorithms, a model will be developed to classify news articles as either true or false. This model will utilize tools such as Python's scikit-learn and NLP for textual analysis. The process will involve feature extraction and vectorization, with a focus on utilizing Python's scikit-learn library for text data tokenization and feature extraction, leveraging tools like Count Vectorizer and Tiff Vectorizer. Additionally, feature selection methods will be employed to identify the most relevant features for optimal precision, as determined by the results of the confusion matrix

An adaptive hybrid african vultures-aquila optimizer with Xgb-Tree algorithm for fake news detection

Online platforms and social networking have increased in the contemporary years. They are now a major news source worldwide, leading to the online proliferation of Fake News (FNs). These FNs are alarming because they fundamentally reshape public opinion, which may cause customers to leave these online platforms, threatening the reputations of several organizations and industries. This rapid dissemination of FNs makes it imperative for automated systems to detect them, encouraging many researchers to propose various systems to classify news articles and detect FNs automatically. In this paper, a Fake News Detection (FND) methodology is presented based on an effective IBAVO-AO algorithm, which stands for hybridization of African Vultures Optimization (AVO) and Aquila Optimization (AO) algorithms, with an extreme gradient boosting Tree (Xgb-Tree) classifier. The suggested methodology involves three main phases: Initially, the unstructured FNs dataset is analyzed, and the essential features are extracted by tokenizing, encoding, and padding the input news words into a sequence of integers utilizing the GLOVE approach. Then, the extracted features are filtered using the effective Relief algorithm to select only the appropriate ones. Finally, the recovered features are used to classify the news items using the suggested IBAVO-AO algorithm based on the Xgb-Tree classifier. Hence, the suggested methodology is distinguished from prior models in that it performs automatic data pre-processing, optimization, and classification tasks. The proposed methodology is carried out on the ISOT-FNs dataset, containing more than 44 thousand multiple news articles divided into truthful and fake. We validated the proposed methodology’s reliability by examining numerous evaluation metrics involving accuracy, fitness values, the number of selected features, Kappa, Precision, Recall, F1-score, Specificity, Sensitivity, ROC_AUC, and MCC. Then, the proposed methodology is compared against the most common meta-heuristic optimization algorithms utilizing the ISOT-FNs. The experimental results reveal that the suggested methodology achieved optimal classification accuracy and F1-score and successfully categorized more than 92.5% of news articles compared to its peers. This study will assist researchers in expanding their understanding of meta-heuristic optimization algorithms applications for FND.Graphical

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

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.

Attention Mechanism Based on a Pre-trained Model for Improving Arabic Fake News Predictions

Social media and news agencies are major sources for tracking news and events. With these sources' massive amounts of data, it is easy to spread false or misleading information. Given the great dangers of fake news to societies, previous studies have given great attention to detecting it and limiting its impact. As such, this work aims to use modern deep learning techniques to detect Arabic fake news. In the proposed system, the attention model is adapted with bidirectional long-short-term memory (Bi-LSTM) to identify the most informative words in the sentence. Then, a multi-layer perceptron (MLP) is applied to classify news articles as fake or real. The experiments are conducted on a newly launched Arabic dataset called the Arabic Fake News Dataset (AFND). The AFDN dataset contains exactly 606912 news articles collected from multiple sources, so it is suitable for deep learning requirements. Both simple recurrent neural networks (S-RNN), long short-term memory (LSTM), and gated recurrent units (GRU) are used for comparison. According to evaluation criteria, our proposed model achieved an accuracy of (0.8127), which is the best and highest accuracy among the deep learning methods used in this work. Moreover, the performance of our proposed model is better compared to previous studies, which used the AFND.

Exchange rate expectations and exports: Firm‐level evidence from China

AbstractThe notion that the exchange rate affects exports is well understood. However, whether exporters respond to the expectations of the exchange rate is unknown. Hence, in this study, we construct a measure of exchange rate expectations based on news articles from the Factiva database. We use machine learning to identify and classify news articles about the appreciation of the renminbi (RMB, Chinese currency). Our empirical estimation shows that from 2000 to 2006, Chinese firms reduced their exports in response to a higher expectation of RMB appreciation. They switched their sales from export to domestic markets. The responses are larger in low‐productivity firms, state‐owned enterprises, processing trade, and final goods trade.

Empirical Configuration Study of a Common Document Clustering Pipeline

Document clustering is frequently used in applications of natural language processing, e.g. to classify news articles or creating topic models. In this paper, we study document clustering with the common clustering pipeline that includes vectorization with BERT or Doc2Vec, dimension reduction with PCA or UMAP, and clustering with K-Means or HDBSCAN. We discuss the inter- actions of the different components in the pipeline, parameter settings, and how to determine an appropriate number of dimensions. The results suggest that BERT embeddings combined with UMAP dimension reduction to no less than 15 dimensions provides a good basis for clustering, regardless of the specific clustering algorithm used. Moreover, while UMAP performed better than PCA in our experiments, tuning the UMAP settings showed little impact on the overall performance. Hence, we recommend configuring UMAP so as to optimize its time efficiency. According to our topic model evaluation, the combination of BERT and UMAP, also used in BERTopic, performs best. A topic model based on this pipeline typically benefits from a large number of clusters.

Strategies for enhancing the performance of news article classification in Bangla: Handling imbalance and interpretation

The rapid increase in obtainable online text data has made text categorization an important tool for data analysts to extract relevant information on the web. However, incorrect or incomplete classification of marginalized groups may result from using biased text data. In order to remedy the disparity in available data, this research suggests a system for classifying and analyzing Bangla news articles. The suggested approach first uses both Random Under-Sampling (RUS) and Synthetic Minority Oversampling Techniques to balance the massive unbalanced Bangla News dataset consisting of 4,37,948 instances (SMOTE). Secondly, the proposed system employs three machine learning models: Logistic Regression, Decision Tree, and Stochastic Gradient Descent along with three deep learning models: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Bidirectional Encoder Representations from Transformers (BERT) for Bangla text categorization. The experimental results signify the superior performance of BERT to other classification models of the system as well as other existing methods in this domain. The proposed system achieves the maximum accuracy of 99.04% in balanced dataset and 72.23% in imbalanced dataset using BERT. K-fold cross validation with varied K values is used to determine the performance consistency of BERT. Finally, both LIME (Local Interpretable Model agnostic Explanations and SHAP (SHapley Additive exPlanations) techniques are applied for interpreting each prediction made by BERT.

Arabic News Classification Based on the Country of Origin Using Machine Learning and Deep Learning Techniques

With the rise of Arabic news articles published daily, people are becoming increasingly concerned about following the news from reliable sources, especially regarding events that impact their country. To assess a news article’s significance to the user, it is essential to identify the article’s country of origin. This paper proposes several classification models that categorize Arabic news articles based on their country of origin. The models were developed using comprehensive machine learning and deep learning techniques with several feature training methods. The results show the ability of our model to classify news articles based on their country of origin, with close accuracy between machine learning and deep learning techniques of up to 94%.

CABLE NEWS NETWORK (CNN) ARTICLES CLASSIFICATION USING RANDOM FOREST ALGORITHM WITH HYPERPARAMETER OPTIMIZATION

The growth of news articles on the internet occurs in a short period with large amounts so necessary to be grouped into several categories for easy access. There is a method for grouping news articles, namely classification. One of the classification methods is random forest which is built on decision tree. This research discusses the application of random forest as a method of classifying news articles into six categories, these are business, entertainment, health, politics, sport, and news. The data used is Cable News Network (CNN) articles from 2011 to 2022. The data is in form of text and has large amounts so good handling is needed to avoid overfitting and underfitting. Random forest is proper to apply to the data because the algorithm works very well on large amounts of data. However, random forest has a difficult interpretation if the combination of parameters is not appropriate in the data processing. Therefore, hyperparameter optimization is needed to discover the best combination of parameters in the random forest. This research uses search cross-validation (SearchCV) method to optimize hyperparameters in the random forest by testing the combinations one by one and validating those. Then we obtain the classification of news articles into six categories with an accuracy value of 0.81 on training and 0.76 on testing.

Comparison analysis of Bangla news articles classification using support vector machine and logistic regression

In the information age, Bangla news articles on the internet are fast-growing. For organizing, every news site has a particular structure and categorization. News article classification is a method to determine a document’s classification based on various predefined categories. This research discusses the classification of Bangla news articles on the online platform and tries to make constructive comparison using several classification algorithms. For Bangla news articles classification, term frequencyinverse document frequency (TF-IDF) weighting and count vectorizer have been used as a feature extraction process, and two common classifiers named support vector machine (SVM) and logistic regression (LR) employed for classifying the documents. It is clear that the accuracy of the experimental results by applying SVM is 84.0% and LR is 81.0% for twelve categories of news articles. In this research work, when we have made comparison two renowned classification algorithms applied on the Bangla news articles, LR was outperformed by SVM.

MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification

This article presents a dataset of 10,917 news articles with hierarchical news categories collected between 1 January 2019 and 31 December 2019. We manually labeled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories. This dataset can be used to train machine learning models for automatically classifying news articles by topic. This dataset can be helpful for researchers working on news structuring, classification, and predicting future events based on released news.

ESG controversies and investor trading behavior in the Korean market

This study examines how investor trading behavior changes following environmental, social, and governance (ESG) controversies by analyzing textual news data. We use deep-learning-based natural language processing to classify news articles into specific categories of controversy. ESG controversies generally increase investors’ trading activities regardless of their type, while their reactions differ by ESG pillar. Interestingly, domestic institutions tend to sell stocks with controversies.

Interactive Web App for Fake News Detection

In the contemporary era of technology, individuals who utilize mobile phones and laptops have developed a preference for accessing news through online media. News organizations disseminate news and offer confirmation sources. However, the issue at hand is how to authenticate stories and articles shared on social networks such as WhatsApp groups, Facebook pages, Twitter, and other smaller blogs and social networking sites. It is hazardous for society to accept rumours disguised as news, especially in developing nations like India, where it is crucial to prevent rumours and specialize in honest and verified information. Classifying written articles as misleading or deceptive is not easy to automate, and even experts in a specific field must evaluate several factors before rendering a judgment regarding the validity of a message. This project proposes the use of a machine learning approach to automatically classify news articles. This endeavour explores numerous text characteristics that can be employed to differentiate fabricated news content from actual news.

Enhancing the Fake News Classification Model Using Find-Tuning Approach

In recent years, fake news on social media has become a pressing concern, posing significant threats to individuals, organizations, and society as a whole. we present a novel strategy to enhance the accuracy of fake news classification models through fine-tuning. Our proposed model involves adding new layers and freezing some layers in the BEART model, resulting in improved performance. To facilitate our research, we constructed a comprehensive fake news dataset by combining real and fake datasets obtained from secondary sources. Firstly, the dataset underwent rigorous pre-processing, including data cleaning, text normalization, tokenization, stop word removal, and other techniques, ultimately enabling binary classification. Subsequently, the proposal model (DCNN) was trained on this dataset to classify news articles as either real or fake. Notably, experimental results demonstrate that our approach outperforms several recent studies in detecting fake news, achieving high accuracy. To evaluate the effectiveness of our proposed model, we employed various evaluation methods. Firstly, we utilized the Tag Cloud technique, which visually represents the most frequently used words in the text or documents, enabling us to distinguish between real and fake news. Additionally, we employed the Classification report, which provides precision, F1, recall, and support scores, to comprehensively assess the model's performance. Furthermore, we employed the confusion matrix, a tabular layout that effectively visualizes the classification algorithm's performance, thereby enabling a clear interpretation based on known true values. Therefore, the proposed model was trained to classify news articles as real or fake, and the experiments on the dataset show that this approach performs better than several recent studies for detecting fake news, achieving high accuracy.

Machine Learning-Based Approach for Fake News Detection

In the modern era where the internet is found everywhere and there is rapid adoption of social media which has led to the spread of information that was never seen within human history before. This is due to the usage of social media platforms where consumers are creating and sharing more information where most of them are misleading with no relevance with reality. Classifying the text article automatically as misinformation is a bit challenging task. This development addresses how automated classification of text articles can be done. We use a machine learning approach for the classification of news articles. Our study involves exploring different textual properties that may be often used to distinguish fake contents from real ones. By using those properties, can train the model with different machine learning algorithms and evaluate their performances. The classifier with the best performance is used to build the classification model which predicts the reliability of the news articles present in the dataset.

Teachable Conversational Agents for Crowdwork: Effects on Performance and Trust

Traditional crowdsourcing has mostly been viewed as requester-worker interaction where requesters publish tasks to solicit input from human crowdworkers. While most of this research area is catered towards the interest of requesters, we view this workflow as a teacher-learner interaction scenario where one or more human-teachers solve Human Intelligence Tasks to train machine learners. In this work, we explore how teachable machine learners can impact their human-teachers, and whether they form a trustable relation that can be relied upon for task delegation in the context of crowdsourcing. Specifically, we focus our work on teachable agents that learn to classify news articles while also guiding the teaching process through conversational interventions. In a two-part study, where several crowd workers individually teach the agent, we investigate whether this learning by teaching approach benefits human-machine collaboration, and whether it leads to trustworthy AI agents that crowd workers would delegate tasks to. Results demonstrate the benefits of the learning by teaching approach, in terms of perceived usefulness for crowdworkers, and the dynamics of trust built through the teacher-learner interaction.

Classification of Articles from Mass Media by Categories and Relevance of the Subject Area

The research is devoted to classification of news articles about P. G. Demidov Yaroslavl State University (YarSU) into 4 categories: “society”, “education”, “science and technologies”, “not relevant”.The proposed approaches are based on using the BERT neural network and methods of machine learning: SVM, Logistic Regression, K-Neighbors, Random Forest, in combination of different embedding types: Word2Vec, FastText, TF-IDF, GPT-3. Also approaches of text preprocessing are considered to achieve higher quality of the classification. The experiments showed that the SVM classifier with TF-IDF embedding and trained on full article texts with titles achieved the best result. Its micro-F-measure and macro-F-measure are 0.8214 and 0.8308 respectively. The BERT neural network trained on fragments of paragraphs with YarSU mentions, from which the first 128 words and the last 384 words were taken, showed comparable results. The resulting micro-F-measure and macro-F-measure are 0.8304 and 0.8181 respectively. Thus, using paragraphs with the target organisation mentions is enough to classify text by categories efficiently.