Classification Of Text Data Research Articles

Classification of Fake News Using Deep Learning-Based GloVE-LSTM Model

Fake news is deliberately created with the goal of influencing people and their belief systems. Because false news has a detrimental influence on society and politics, it has become increasingly crucial to identify and stop it from spreading. Most of the prior research has employed supervised learning but has placed emphasis on the terms that were used in the dataset. Initially, we began by pre-processing the information (replacing the missing value, noise removal, tokenization, and stemming). LSTM (long short-term memory network) model is employed for text data classification in this article, and we deal with automated feature selection from text data using the GloVe model. Unlike previous models, the suggested model can select the relevant attributes to determine whether the news is false or real while existing models fail in this regard. The proposed model outperforms the already available models.

Textual adversarial attacks by exchanging text‐self words

Adversarial attacks expose the vulnerability of deep neural networks. Compared to image adversarial attacks, textual adversarial attacks are more challenging due to the discrete nature of texts. Recent synonym-based methods achieve the current state-of-the-art results. However, these methods introduce new words against the original text, leading to that humans easily perceive the difference between the adversarial example and the original text. Motivated by the fact that humans are usually unaware of chaotic word order in some cases, we propose exchange-attack (EA), a concise and effective word-level textual adversarial attack model. Specifically, the EA model generates adversarial examples by exchanging words of the original text itself according to the contributions that these words make regarding classification results. Intuitively, the smaller the distance between the two exchanged words, the more difficult the chaotic word order to be perceived by humans. We thus take the word distance into consideration when generating the chaotic word orders. Extensive experiments on several text classification data sets show that the EA model consistently outperforms the selected baselines in terms of averaged after-attack accuracy, modification rate, query number, and semantic similarity. And human evaluation results reveal that humans difficultly perceive the adversarial examples generated by the EA model. In addition, quantitative and qualitative analyses further validate the effectiveness of the EA model, including that the generated adversarial examples are grammatically correct and semantically preserved.

An Earthquake Emergency Web Data Cleaning and Classification Method Based on Word Frequency and Position Weighting.

The speed of earthquake emergency web document data cleaning is one of the key factors affecting emergency rescue decision-making. Data classification is the core process of data cleaning, and the efficiency of data classification determines the speed of data cleaning. This article is based on earthquake emergency Web document data and HTML structural features, combined with TF-IDF Algorithm and information calculation model, improves the word frequency factor and location factor parameters, and proposes the weighted frequency algorithm P-TF-IDF for earthquake emergency Web documents. To filter out less frequent words and optimize the FastText model, N-gram Feature word vectors effectively improve the efficiency of Web document data classification; for text classification data, use missing data recognition rules, data classification rules, and data repair rules to design an artificial intelligence-based earthquake emergency network information data cleaning framework to detect invalid data sets value, complete data comparison and redundancy judgment, clean up data conflicts and data errors, and generate a complete data set without duplication. The data cleaning framework not only completes the fusion of earthquake emergency network information but also provides a data foundation for the visualization of earthquake emergency data.

A Combined Approach for Multi-Label Text Data Classification.

Automated data analysis solutions are very dependent on data and its quality. The possibility of assigning more than one class to the same data item is one of the specificities that need to be taken into account. There are no solutions, dedicated to Lithuanian text data classification that helps to assign more than one class to data item. In this paper, a new combined approach has been proposed for multilabel text data classification for text analysis. The main aim of the proposed approach is to improve the accuracy of traditional classification algorithms by incorporating the results obtained using similarity measures. The experimental investigation has been performed using the financial news multilabel text data in the Lithuanian language. Data have been collected from four public websites and classified by experts into ten classes manually, where each of the data items has no more than two classes. The results of five commonly used algorithms have been compared for dataset classification: the support vector machine, multinomial naive Bayes, k-nearest neighbours, decision trees, linear and discriminant analysis. In addition, two similarity measures have been compared: the cosine distance and the dice coefficient. Research has shown that the best results have been obtained using the cosine similarity distance and the multinomial naive Bayes classifier. The proposed approach combines the results of these two methods. Research on different cases of the proposed approach indicated the peculiarities of its application. At the same time, the combined approach allowed us to obtain a statistically significant increase in global accuracy.

A Method for Classifying Information in Education Policy Texts Based on an Improved Attention Mechanism Model

Text classification techniques in natural language processing can automatically classify text data in a more efficient way, saving human resources and costs. Therefore, text classification techniques can be applied to the automatic classification of education policy data to quickly locate and accurately find education policy data, thus realising the information management and visual analysis of education policy data. This paper proposes a text classification algorithm based on the attention mechanism of headline and body text according to the characteristics of education policy and introduces this algorithm in detail through a model diagram. The experimental comparison with existing classification algorithms verifies the superiority of the algorithm in the classification method of education policy text classification information.

An Efficient Data Classification Decision Based on Multimodel Deep Learning

A single model is often used to classify text data, but the generalization effect of a single model on text data sets is poor. To improve the model classification accuracy, a method is proposed that is based on a deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) and integrates multiple models trained by a deep learning network architecture to obtain a strong text classifier. Additionally, to increase the flexibility and accuracy of the model, various optimizer algorithms are used to train data sets. Moreover, to reduce the interference in the classification results caused by stop words in the text data, data preprocessing and text feature vector representation are used before training the model to improve its classification accuracy. The final experimental results show that the proposed model fusion method can achieve not only improved classification accuracy but also good classification effects on a variety of data sets.

Multi-label learning with missing features and labels and its application to text categorization

• This paper proposes a new method MMFL to address the problem of missing features and missing labels simultaneously in multi-label learning. • Matrix decomposition technology is used to recover the missing values of features and labels. In addition, to overcome the problem of tail labels in matrix factorization, an extra classifier for the sparse tail labels is built. • The experiment on nine multi-label text categorization data sets show that our proposed method achieves a competitive performance in multi-label learning with missing features and labels. In multi-label learning, researchers usually assume that the training data set is complete. However, this assumption is not always hold in real applications, e.g., the features or labels are missing for some data examples. Existing algorithms mainly focus on the problem of missing labels, and ignore the problem of missing features. This paper proposes a novel multi-label learning algorithm named MMFL, i.e., Multi-label learning with Missing Features and Labels, which can deal with the problem of missing features and labels simultaneously. First, we try to recover the missing values of features and labels by matrix factorization, and then learn a classification model from the latent feature space to the latent label space. Second, to overcome the problem of tail labels in matrix factorization, we build an extra classifier for the sparse tail labels. Besides, the manifold regularization technology is used to keep the manifold structures of instance similarity and label correlation. The effectiveness of our proposed method is verified by comparing it with the state-of-the-art approaches over eight multi-label benchmark data sets.

Implementasi Text Mining Terhadap Analisis Sentimen Masyarakat Dunia Di Twitter Terhadap Kota Medan Menggunakan K-Fold Cross Validation Dan Naïve Bayes Classifier

The city of Medan is one of the largest cities in Indonesia which has tourist charm, culinary and also supporting facilities and infrastructure that are quite adequate in the city, and of course will be ogled by foreign or foreign tourism. So it is necessary to do research in order to find out how the public sentiment towards tourism in the city of Medan, whether the majority of the world community considers it positive or negative. The response of the world community regarding Medan tourism is obtained from the Application Programming Interface (API) on Twitter because social media has a lot of users in the world. Indonesia has even reached 19.5 million users out of a total of 300 million users worldwide. In this study, one part of the Text Mining algorithm is text preprocessing, the text preprocessing used is case folding, tokenizing, stopwords, and stemming. For the preprocessing stemming, the snowball stemmer algorithm is used, while the analysis of the text data classification uses the Naïve Bayes Classifier algorithm and to partition the data the K-Fold Cross Validation method will be used. The data used in this study is a collection of tweets about tourism in the city of Medan on December 1, 2019 until December 8, 2019. Data is obtained from the Twitter API (Application Programming Interface) as many as 2000 tweets. Extracting data from Twitter via Twitter API (Application Programming Interface) using RStudio as a console for the data retrieval process

A Novel Approach of Feature Space Reconstruction with Three-Way Decisions for Long-Tailed Text Classification.

Text classification is widely studied by researchers in the natural language processing field. However, real-world text data often follow a long-tailed distribution as the frequency of each class is typically different. The performance of current mainstream learning algorithms in text classification suffers when the training data are highly imbalanced. The problem can get worse when the categories with fewer data are severely undersampled to the extent that the variation within each category is not fully captured by the given data. At present, there are a few studies on long-tailed text classification which put forward effective solutions. Encouraged by the progress of handling long-tailed data in the field of image, we try to integrate effective ideas into the field of long-tailed text classification and prove the effectiveness. In this paper, we come up with a novel approach of feature space reconstruction with the help of three-way decisions (3WDs) for long-tailed text classification. In detail, we verify the rationality of using a 3WD model for feature selection in long-tailed text data classification, propose a new feature space reconstruction method for long-tailed text data for the first time, and demonstrate how to effectively generate new samples for tail classes in reconstructed feature space. By adding new samples, we enrich the representing information of tail classes, to improve the classification results of long-tailed text classification. After some comparative experiments, we have verified that our model is an effective strategy to improve the performance of long-tailed text classification.

A Multi-Level Optimization Framework for End-to-End Text Augmentation

AbstractText augmentation is an effective technique in alleviating overfitting in NLP tasks. In existing methods, text augmentation and downstream tasks are mostly performed separately. As a result, the augmented texts may not be optimal to train the downstream model. To address this problem, we propose a three-level optimization framework to perform text augmentation and the downstream task end-to- end. The augmentation model is trained in a way tailored to the downstream task. Our framework consists of three learning stages. A text summarization model is trained to perform data augmentation at the first stage. Each summarization example is associated with a weight to account for its domain difference with the text classification data. At the second stage, we use the model trained at the first stage to perform text augmentation and train a text classification model on the augmented texts. At the third stage, we evaluate the text classification model trained at the second stage and update weights of summarization examples by minimizing the validation loss. These three stages are performed end-to-end. We evaluate our method on several text classification datasets where the results demonstrate the effectiveness of our method. Code is available at https://github.com/Sai-Ashish/End-to-End-Text-Augmentation.

Methodology for solving problems of classification of appeals/requests of citizens to the “hotline” of the President of the Russian Federation

The use of neural networks for the classification of text data is an important area of digital transformation of socio-economic systems. The article is devoted to the description of the methodology for classifying citizens' appeals. The proposed technique involves the use of a convolutional neural network. The stages of processing citizens' appeals in the amount of 7000 appeals are described. In order to reduce the dimension of the problem, methods of filtering and removing stop words were applied. The resulting data set allows you to choose the best classifier in terms of accuracy, specificity, sensitivity. Training and test samples were used, as well as cross-validation. The article shows the effectiveness of using this method to distribute requests on 15 topics of citizens' appeals to the “hotline” of the President of the Russian Federation. Automating the classification of received appeals by topic allows them to be processed quickly for further study by the relevant departments. The purpose of the study is automation of the distribution of citizens' appeals to the President's hotline by category based on the use of modern machine learning methods. Materials and methods. The development of software that automates the process of distributing citizens into categories is carried out using a convolutional neural network written in the Python programming language. Results. With the help of the prepared data set, the pre-trained model of NL BERT and sciBERT was trained by the deep learning method. The model shows an accuracy of 86% in the estimates of quality metrics. Conclusion. A pre-trained model was trained using a convolutional neural model using a prepared data set. Even if the forecast does not match the real category, the model gives a minor error, correctly determines the category of the appeal. The results obtained can be recommended for practical application by authors of scientific publications, scientific institutions, editors and reviewers of publishing houses.

Application of classification models on maintenance records through text mining approach in industrial environment

PurposeThe data created from regular maintenance activities of equipment are stored as text in industrial plants. The size of these data is increasing rapidly nowadays. Text mining provides a chance to handle this huge amount of text data and extract meaningful information to improve various processes of an industrial environment. This paper represents the application of classification models on maintenance text records to classify failure for improving maintenance programs in the industry.Design/methodology/approachThis paper is presented as an implementation study, where text mining approaches are used for binary classification of text data. Naive Bayes and Support Vector Machine (SVM), two classification algorithms are applied for training and testing of the models as per the labeled data. The reason behind this is, these algorithms perform better on text data for classifying failure and they are easy to handle. A methodology is proposed for the development of maintenance programs, including classification of potential failure in advance by analyzing the regular maintenance data as well as comparing the performance of both models on the data.FindingsThe accuracy of both models falls within the acceptable limit, and performance evaluation of the models concludes the validation of the results. Other performance measures exhibit excellent values for both of the models.Practical implicationsThe proposed approach provides the maintenance team an opportunity to know about the upcoming breakdown in advance so that necessary measures can be taken to prevent failure in an industrial environment. As predictive maintenance incurs a high expense, it could be a better replacement for small and medium industrial plants.Originality/valueNowadays, maintenance is preventive-based rather than a corrective approach. The proposed technique is facilitating the concept of a proactive approach by minimizing the cost of additional maintenance steps. As predictive maintenance is efficient but incurs high expenses, this proposed method can minimize unnecessary maintenance operations and keep control over the budget. This is a significant way of developing maintenance programs and will make maintenance personnel ready for the machine breakdown.

РАЗРАБОТКА WEB-ПРИЛОЖЕНИЯ ДЛЯ АНАЛИЗА НАСТРОЕНИЙ ТЕКСТА С ПОМОЩЬЮ ФРЕЙМВОРКА FLASK И ЯЗЫКА PYTHON

Introduction. Currently, of great practical and scientific interest is the solution of the problems of determining the moods of user texts transmitted in the information space of the Internet and in other networks. Sentiment analysis is used by researchers and marketers to study user feedback. For example, at Russian Railways, such an analysis can contribute to the study of public opinion in the interests of improving the quality of passenger transportation. Based on the study of public opinion, for example, it is possible to study and improve the quality of education in universities. The purpose of the study is to develop an effective technology for the use of modern tools for developing a Web-application that allows you to analyze the sentiment of user text. Methods and means. The web interface corresponding to the tasks is implemented using the Flask framework, and the training model is based on the use of the TensorFlow library. Results. The rationale for the full cycle of creating a training model based on open data of Twitter posts using the TensorFlow library has been performed, and the ability to automatically select parameters using the Keras Tuner add-in has been implemented. Practical significance. The main distinguishing feature of the developed application is the presence of a user interface that allows you to control the predictive and training blocks of the program without the need to manually launch the corresponding sections of the code. The necessity and convenience of working with a trained model for classifying text through a graphical Web interface are shown. This approach allows you to reduce the time for performing routine technical manipulations when processing a large amount of data. Having a trained model and an interface for working with it makes it possible to skip the training stages and go directly to the stage of predicting user values. Discussion. When solving the problem, we studied the process of creating a data classification model based on the TensorFlow module, as well as creating a neural network using the Keras add-on. In the process of training, the best model was selected for the parameter-accuracy on the test sample-using Keras Tuner. A web interface in Flask was developed, which is isolated by a virtual Python runtime. It is advisable to continue further research in the areas of practical use of the results obtained in the interests of improving the quality of training and retraining of personnel at Russian Railways, improving the characteristics of the classification of text data based on the use of text corpora to set up a tone classifier, as well as developing a method for preliminary data cleaning to create a neural network model based on the use of modern and lightweight libraries for the possibility of preliminary preparation of the text for training.

A Comparison of Multi-Label Text Classification Models in Research Articles Labeled With Sustainable Development Goals

The classification of scientific articles aligned to Sustainable Development Goals is crucial for research institutions and universities when assessing their influence in these areas. Machine learning enables the implementation of massive text data classification tasks. The objective of this study is to apply Natural Language Processing techniques to articles from peer-reviewed journals to facilitate their classification according to the 17 Sustainable Development Goals of the 2030 Agenda. This article compares the performance of multi-label text classification models based on a proposed framework with datasets of different characteristics. Results reveal that a particular combination of a transformation method with a classifier algorithm dominates the performance results.

An Integration Model for Text Classification using Graph Convolutional Network and BERT

Recently, Graph Convolutional Neural Network (GCN) is widely used in text classification tasks, and has effectively completed tasks that are considered to have a rich relational structure. However, due to the sparse adjacency matrix constructed by GCN, GCN cannot make full use of context-dependent information in text classification, and cannot capture local information. The Bidirectional Encoder Representation from Transformers (BERT) has been shown to have the ability to capture the contextual information in a sentence or document, but its ability to capture global information about the vocabulary of a language is relatively limited. The latter is the advantage of GCN. Therefore, in this paper, Mutual Graph Convolution Networks (MGCN) is proposed to solve the above problems. It introduces semantic dictionary (WordNet), dependency and BERT. MGCN uses dependency to solve the problem of context dependence and WordNet to obtain more semantic information. Then the local information generated by BERT and the global information generated by GCN are interacted through the attention mechanism, so that they can influence each other and improve the classification effect of the model. The experimental results show that our model is more effective than previous research reports on three text classification data sets.

A graph neural network fused with multi-head attention for text classification

Graph neural network (GNN) has done a good job of processing intricate architecture and fusion of global messages, research has explored GNN technology for text classification. However, the model that fixed the entire corpus as a graph in the past faced many problems such as high memory consumption and the inability to modify the construction of the graph. We propose an improved model based on GNN to solve these problems. The model no longer fixes the entire corpus as a graph but constructs different graphs for each text. This method reduces memory consumption, but still retains global information. We conduct experiments on the R8, R52, and 20newsgroups data sets, and use accuracy as the experimental standard. Experiments show that even if it consumes less memory, our model accomplish higher than existing models on multiple text classification data sets.

Research on Algorithm of DRC Catalog Generation Based on Machine Learning

Aiming at the current logic and expansion issues in data governance caused by data scheduling in two directions: business retrieval and data processing. Data Oriented Architecture proposes a Registration Intermediate Library(RIL) and a Catalog Intermediate Library(CIL) to achieve separation of management and application. Among them, the business-oriented catalog intermediate library automatically classifies and labels data registration information through machine learning, and realizes the automatic generation of catalogs.There are many text classification methods based on machine learning, but the model obtained through unbalanced data set training often has performance degradation. In order to solve this problem, category weights are introduced in the feature vector to reduce the influence of most samples on the model parameters. Experiments on THUCNews text classification data set show that the proposed method can effectively improve the performance of baseline system, solve the problem of unbalanced training data categories and solve the problem of automatic generation of DRC directory.

The Research Progress of the Deep Hybrid Model in the Field of Text Classification

The traditional deep convolution neural network model cannot extract the context information effectively in dealing with complex long text data sets, and it is difficult to obtain the deep semantic information of the text. The deep hybrid neural network can optimize and improve the local feature extraction ability of CNN model while preserving the ability of local feature extraction. It has achieved good performance on complex data sets, so it has been paid more and more attention by researchers. Firstly, this article sorts out the current mainstream text classification data sets. Secondly, the model of the hybrid neural network based on the convolutional neural network construction is as follows: the improvement of the CNN model; technology fusion based on CNN model and the CNN-based model mixing three categories carry out analysis and sorting. Finally summarize the current problems in the text classification field, and look forward to future development and research.

Research on English Vocabulary Classification Based on Computer Deep Learning

With the popularity of the Internet, the analysis and classification of text data has become a research hotspot in recent years, and text classification is one of them. In this paper, cbow algorithm is used to train the word vector based on deep learning, and fast text is used for text classification experiment.

Full Content-based Web Page Classification Methods by using Deep Neural Networks

The quality of the web page classification process has a huge impact on information retrieval systems. In this paper, we proposed to combine the results of text and image data classifiers to get an accurate representation of the web pages. To get and analyse the data we created the complicated classifier system with data miner, text classifier, and aggregator. The process of image and text data classification has been achieved by the deep learning models. In order to represent the common view onto the web pages, we proposed three aggregation techniques that combine the data from the classifiers.