Traditional Text Classification Research Articles

Research on Model and Algorithm of Multiview and Multilabel Classification Based on Nearest-Neighbor Model

In the field of machine learning, multilabel learning gradually evolved from the traditional text classification problem. In this study, a multiview multilabel via optimal classifier chain (MVMLOCC) algorithm based on the nearest-neighbor model is proposed. The algorithm model establishes a multilabel chain classifier for each view of the dataset and predicts unknown data samples by dynamically adjusting the weights of chain learners. When an unknown example is input, multiple chain classifier models are multiplied by corresponding weights to obtain the final tag set situation. The model makes full use of the relevance between multiple tags and the complementarity and integrity between multiple perspectives and can achieve better learning results. The final experimental results show that our proposed model can be applied to different multilabel classification tasks and has achieved excellent performance under different evaluation indicators. This study mainly studies the multiview and multilabel classification method and uses active learning technology to solve the problem of high label components in data collection.

Classification of legal articles based on machine learning

In order to solve the problem of long execution time of traditional legal text classification, this paper designs a legal text classification method based on machine learning. Based on the combination of support vector machine (SVM) and naive Bayes classifier in machine learning, the feature value of legal text is filtered, the legal text classification matrix is established, and the legal text classification results are accessed directly through embedded system to realize the legal text classification. The experimental results show that the implementation time of the design method is more than twice as fast as that of the control group, which can solve the problem of long implementation time of the traditional method.

Digital Library Information Integration System Based on Big Data and Deep Learning

In order to solve the defects of traditional text classification in digital library, the author proposes a method based on deep learning in the field of big data and artificial intelligence, which is applied to the digital library information integration system. On the basis of systematically sorting out the traditional text classification of digital library of this method, this paper proposes a digital library text classification model based on deep learning and uses the word vector method to represent text features, the convolutional neural network in the deep learning model is used to extract the essential features of text information, and experimental verification is carried out. Experimental results show that deep learning-based text classification model can effectively improve the accuracy (average 94.8%) and recall (average 94.5%) of text classification in digital libraries; compared with the traditional text classification method, the text classification method based on deep learning improves the average F1 value by about 11.6%. Conclusion. This method can not only improve the intelligence of the internal business of the digital library, but also improve the efficiency and quality of the information service of the digital library.

Contrast-Enhanced Semi-supervised Text Classification with Few Labels

Traditional text classification requires thousands of annotated data or an additional Neural Machine Translation (NMT) system, which are expensive to obtain in real applications. This paper presents a Contrast-Enhanced Semi-supervised Text Classification (CEST) framework under label-limited settings without incorporating any NMT systems. We propose a certainty-driven sample selection method and a contrast-enhanced similarity graph to utilize data more efficiently in self-training, alleviating the annotation-starving problem. The graph imposes a smoothness constraint on the unlabeled data to improve the coherence and the accuracy of pseudo-labels. Moreover, CEST formulates the training as a “learning from noisy labels” problem and performs the optimization accordingly. A salient feature of this formulation is the explicit suppression of the severe error propagation problem in conventional semi-supervised learning. With solely 30 labeled data per class for both training and validation dataset, CEST outperforms the previous state-of-the-art algorithms by 2.11% accuracy and only falls within the 3.04% accuracy range of fully-supervised pre-training language model fine-tuning on thousands of labeled data.

Text- and author-dependent moral foundations classification

ABSTRACT Moral Foundations Theory (MFT) has attracted a great deal of attention in Natural Language Processing (NLP) and social media analysis, including both applications that attempt to infer moral values inference from text, or otherwise use moral foundations information to perform another downstream task. In this work, we address the issue of moral foundations inference from text data according to two perspectives, hereby called text- and author-dependent classification, by presenting a number of experiments to compare traditional text classifiers with more recent approaches based on pre-trained language models in both English and Portuguese languages. Results suggest that moral foundations classification relies heavily on lexical information, and that different models may be more suitable to each task, and leave a number of opportunities for further research in the field.

Naive Bayesian Prediction of Japanese Annotated Corpus for Textual Semantic Word Formation Classification

With the rapid development of Japanese information processing technology, problems such as polysemy and ambiguity at the text and dialogue level, as well as unregistered words, have become increasingly prominent because computers cannot fully “understand” the semantics of words. How to make the computer “understand” the semantics of words accurately requires the computer to “understand” the rules of converting and integrating words into words from the perspective of semantics. Traditional Japanese text classification mostly adopts the text representation method of vector space model, which has the problem of confusing classification effect. Therefore, this paper proposes the topic of constructing a semantic word formation pattern prediction model based on a large-scale annotated corpus. This paper proposes a solution that combines Japanese semantic word formation rules with pattern recognition algorithms. Aiming at this scheme, a variety of pattern recognition algorithms were compared and analyzed, and the naive Bayesian model was decided to predict semantic word formation patterns. This paper further improves the accuracy of computer prediction of Japanese semantic word formation patterns by adding part of speech. Before modeling, the parts of speech of words are automatically tagged and manually checked based on the original annotated corpus. In the research on predicting Japanese semantic word formation patterns, this paper builds a semantic word formation pattern prediction model based on Naive Bayes and conducts simulation experiments. We divide the eight types of semantic word formation patterns in the annotated corpus into two groups, and divide the obtained sample sets into training sets and test sets, so that the Naive Bayes model first learns semantic word formation rules based on the training sets of each group. Semantic word formation patterns are predicted on the test set for each group. The simulation results show that the prediction model of semantic word formation mode has a generally high degree of fit and prediction accuracy. The prediction model of semantic word formation pattern based on this theory can ensure that the computer can judge the semantic word formation pattern more accurately.

Classifying multi-level product categories using dynamic masking and transformer models

In an online shopping platform, a detailed categorization of the products greatly enhances user navigation. Online retailers also benefit from well-defined product categories as various sales and marketing operations such as special discounts and promotions can be easily done over a set of product categories. Furthermore, incorrect and subjective product categories suggested by an operator can be more easily identified thanks to an automated classification system. In this study, we investigate the task of classifying grocery product categories using product titles. We employ a wide variety of text classification models for this task, including traditional machine learning and deep learning models as well as state-of-the-art transformer models. In our analysis, we specifically focus on the generalizability of the trained classification models to the products of other online retailers, the dynamic masking of infeasible subcategories for pretrained language models, and the impact of incorporating different word embeddings. We observe that the deep learning models and the transformers significantly outperform traditional text classification methods such as XGBoost and SVM, and achieve excellent prediction performance exceeding 90% accuracy and F1-score values. We lastly explore the failure cases where a product is misclassified, and make recommendations for future studies to improve the prediction performance.

Research on the Reconstruction Strategy of Public English Ecological Classroom under the Background of “Internet +”

In the era of “Internet plus,” the educational ecosystem is changing under the influence of modern educational technology, with teachers and students as the main players. The traditional teaching method of “books + classroom + large-class teaching” with teachers at the center has evolved into “two masters: teachers leading and students as the main body.” There are also some discussions in foreign language teaching about educational ecology, with a focus on ecological imbalance. Emotion analysis is divided into two categories based on emotional information: positive and negative. It can be considered a unique text classification, despite the fact that it differs from traditional text classification. Using the emotion recognition algorithm, this paper can provide simple access to WEB pages and carry out emotional experiences such as face emotion recognition. Third-party developers, on the other hand, have a lower entry barrier, allowing them to quickly create colorful applications based on their own ideas.

A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

An essential work in natural language processing is the Multi-Label Text Classification (MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional text classification methods, such as machine learning usually involve data scattering and failure to discover relationships between data. With the development of deep learning algorithms, many authors have used deep learning in MLTC. In this paper, a novel model called Spotted Hyena Optimizer (SHO)-Long Short-Term Memory (SHO-LSTM) for MLTC based on LSTM network and SHO algorithm is proposed. In the LSTM network, the Skip-gram method is used to embed words into the vector space. The new model uses the SHO algorithm to optimize the initial weight of the LSTM network. Adjusting the weight matrix in LSTM is a major challenge. If the weight of the neurons to be accurate, then the accuracy of the output will be higher. The SHO algorithm is a population-based meta-heuristic algorithm that works based on the mass hunting behavior of spotted hyenas. In this algorithm, each solution of the problem is coded as a hyena. Then the hyenas are approached to the optimal answer by following the hyena of the leader. Four datasets are used (RCV1-v2, EUR-Lex, Reuters-21578, and Bookmarks) to evaluate the proposed model. The assessments demonstrate that the proposed model has a higher accuracy rate than LSTM, Genetic Algorithm-LSTM (GA-LSTM), Particle Swarm Optimization-LSTM (PSO-LSTM), Artificial Bee Colony-LSTM (ABC-LSTM), Harmony Algorithm Search-LSTM (HAS-LSTM), and Differential Evolution-LSTM (DE-LSTM). The improvement of SHO-LSTM model accuracy for four datasets compared to LSTM is 7.52%, 7.12%, 1.92%, and 4.90%, respectively.

LSTM-ATTENTION TEXT CLASSIFICATION METHOD COMBINED WITH KEY INFORMATION, 1-7.

Aiming at the problem that the text information could not be fullyunderstood in traditional text classification, this paper proposed arecurrent neural network classification model based on the attentionmechanism combined with key information and gave a sentencepoints extraction method.

Graph Convolutional Network Based on Multi-Head Pooling for Short Text Classification

The short text, sparse features, and the lack of training data, etc. are still the key bottlenecks that restrict the successful application of traditional text classification methods. To address these problems, we propose a Multi-head-Pooling-based Graph Convolutional Network (MP-GCN) for semi-supervised short text classification, and introduce its three architectures, which focus on the node representation learning of 1-order, 1&2-order of isomorphic graphs, and 1-order of heterogeneous graphs, respectively. It only focuses on the structural information of the text graph and does not need pre-training word embedding as the initial node feature. A graph pooling based on self-attention is introduced to evaluate and select important nodes, and the multi-head method is used to provide multiple representation subspaces for pooling without adding trainable parameters. Experimental results demonstrated that, without using pre-training embedding, MP-GCN outperforms state-of-the-art models across five benchmark datasets.

Multi-Label Classification of Research Papers Using Multi-Label K-Nearest Neighbour Algorithm

With the frequent interaction and cooperation between different disciplines in recent years, the number of research papers associated with multiple subjects increased. Correspondingly, some of the existing literatures belong to a single discipline, while others may simultaneously involve more than 2 subjects. At this time, the traditional single-label text classification is not conducive to people obtaining comprehensive and cutting-edge research papers in real life. Thus, it’s of great importance to conduct a multi-label classification of research papers effectively. This paper tests the performance of multi-label learning tasks with text data obtained from the Kaggle website. Firstly, lemmatization and Term Frequency-Inverse Document Frequency (TF-IDF) are used for feature extraction in the pre-processing part. The critical information of text content is statistically analysed, and text content is converted into numerical and high-dimensional vector space. As the traditional single-label classification algorithm is not suitable for the above problem, this paper adopts the Multi-Label K-Nearest Neighbour (ML-KNN) algorithm framework for classification. Experimental results report that the ML-KNN algorithm has achieved better results in multi-label text classification problems than a traditional multi-label algorithm, which proves the effectiveness of the ML-KNN algorithm for text data prediction with multiple subjects. Moreover, the work in this paper is analysed and summarized.

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

This paper uses the database as the data source, using bibliometrics and visual analysis methods, to statistically analyze the relevant documents published in the field of text classification in the past ten years, to clarify the development context and research status of the text classification field, and to predict the research in the field of text classification priorities and research frontiers. Based on the in-depth study of the background, research status, related theories, and developments of online news text classification, this article analyzes the annual publication trend, subject distribution, journal distribution, institution distribution, author distribution, highly cited literature analysis, and research hotspots. Forefront and other aspects clarify the development context and research status of the text classification field and provide a theoretical reference for the further development of the text classification field. Then, on the basis of systematic research on text classification, deep learning, and news text classification theories, a deep learning-based network news text classification model is constructed, and the function of each module is introduced in detail, which will help the future news text classification of application and improvement provide theoretical basis. On the basis of the predecessors, this article separately studied and improved the neural network model based on the convolutional neural network, cyclic neural network, and attention mechanism and merged the three models into one model, which can obtain local associated features and contextual features and highlight the role of keywords. Finally, experiments are used to verify the effectiveness of the model proposed in this paper and compared with traditional text classification to prove the superiority of the network news text classification based on deep learning proposed in this paper. This article aims to study the internal connection between news comments and the number of votes received by news comments, and through the proposed model, the number of votes for news comments can be predicted.

Research on mail classification algorithm based on improved convolutional neural network

Aiming at the problems of high dimension and sparse data in traditional text classification methods, this paper proposes an e-mail classification method based on i-cnn model by combining convolutional neural network (CNN) and inception V1 model. In the convolution and pooling operation, 1 * 1 convolution kernel is added to reduce the thickness of eigenvectors, reduce the parameters and improve the computational performance. Through data validation, i-cnn model for e-mail classification results as high as 92.18%, in the comparative experiment, i-cnn model compared with several machine learning classification models, achieved the highest classification accuracy, in the comparison with or without the emergence structure model, i-cnn model accuracy is higher than CNN model. It shows that the model has a good classification effect, and the integration of inception V1 model can improve the accuracy of text classification.

Weibo Text Classification Based on Knowledge Graph

As a platform for sharing and communication, Weibo can produce a large amount of short text data every day. Because the Weibo text is short and the text features are sparse, it contains less information, serious colloquialization and weak anti-noise ability, so it is difficult to achieve the desired results by using the traditional text classification method to classify Weibo text. This paper proposes a Weibo text classification method based on knowledge graph, which uses knowledge graph to expand text semantics and enrich text features on the basis of the original Weibo text. Then the TextCNN model is used to classify Weibo texts with extended semantics. The experimental results show that the classification accuracy of this method is better than that of Weibo text classification directly.

Word embedding and text classification based on deep learning methods

Traditional manual text classification method has been unable to cope with the current huge amount of data volume. The improvement of deep learning technology also accelerates the technology of text classification. Based on this background, we presented different word embedding methods such as word2vec, doc2vec, tfidf and embedding layer. After word embedding, we demonstrated 8 deep learning models to classify the news text automatically and compare the accuracy of all the models, the model ‘2 layer GRU model with pretrained word2vec embeddings’ model got the highest accuracy. Automatic text classification can help people summary the text accurately and quickly from the mass of text information. No matter in the academic or in the industry area, it is a topic worth discussing.

Machine Learning Based Embedded Code Multi-Label Classification

With the development of Internet of Things (IoT) technology, embedded based electronic devices have penetrated every corner of our daily lives. As the brain of IoT devices, embedded based micro controller unit (MCU) plays an irreplaceable role. The functions of the MCUs are becoming more and more powerful and complicated, which brings huge challenges to embedded programmers. Embedded code, which is highly related to the hardware resources, differs from other popular programming code. The hardware configuration may be a big challenge to the programmers, who may only be good at software development and algorithm design. Online code searching can be time consuming and cannot guarantee an optimal approach. To solve this problem, in this paper, an embedded code classifier, which is designed to help embedded programmers to search for the most efficient code with precise tags, is demonstrated. A high quality embedded code dataset is built. A tag correlated multi-label machine learning model is developed for the embedded code dataset. The experimental results show that the proposed code dataset structure is proved to be more efficient on embedded code classification. The proposed embedded classifier algorithm shows a promising result on embedded code dataset. And it outperforms the traditional machine learning text classification models.

Dual‐Level Attention Based on a Heterogeneous Graph Convolution Network for Aspect‐Based Sentiment Classification

With the development of 5G, the advancement of basic infrastructure has led to considerable development in related research and technology. It also promotes the development of various smart devices and social platforms. More and more people are now using smart devices to post their reviews right after something happens. In order to keep pace with this trend, we propose a method to analyze users’ sentiment by using their text data. When analyzing users’ text data, it is noted that a user’s review may contain many aspects. Traditional text classification methods used by smart devices, however, usually ignore the importance of multiple aspects of a review. Additionally, most algorithms usually ignore the network structure information between the words in a sentence and the sentence itself. To address these issues, we propose a novel dual‐level attention‐based heterogeneous graph convolutional network for aspect‐based sentiment classification which minds more context information through information propagation along with graphs. Particularly, we first propose a flexible HIN (heterogeneous information network) framework to model the user‐generated reviews. This framework can integrate various types of additional information and capture their relationships to alleviate semantic sparsity of some labeled data. This framework can also leverage the full advantage of the hidden network structure information through information propagation along with graphs. Then, we propose a dual‐level attention‐based heterogeneous graph convolutional network (DAHGCN), which includes node‐level and type‐level attentions. The attention mechanisms can analyze the importance of different adjacent nodes and the importance of different types of nodes for the current node. The experimental results on three real‐world datasets demonstrated the effectiveness and reliability of our model.

Transformer based Contextual Model for Sentiment Analysis of Customer Reviews: A Fine-tuned BERT

The Bidirectional Encoder Representations from Transformers (BERT) is a state-of-the-art language model used for multiple natural language processing tasks and sequential modeling applications. The accuracy of predictions from context-based sentiment and analysis of customer review data from various social media platforms are challenging and time-consuming tasks due to the high volumes of unstructured data. In recent years, more research has been conducted based on the recurrent neural network algorithm, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) as well as hybrid, neutral, and traditional text classification algorithms. This paper presents our experimental research work to overcome these known challenges of the sentiment analysis models, such as its performance, accuracy, and context-based predictions. We’ve proposed a fine-tuned BERT model to predict customer sentiments through the utilization of customer reviews from Twitter, IMDB Movie Reviews, Yelp, Amazon. In addition, we compared the results of the proposed model with our custom Linear Support Vector Machine (LSVM), fastText, BiLSTM and hybrid fastText-BiLSTM models, as well as presented a comparative analysis dashboard report. This experiment result shows that the proposed model performs better than other models with respect to various performance measures.

Short text classification based on bidirectional TCN and attention mechanism

The context-related semantic information of the text in the traditional short text classification algorithms are not fully captured, a text classification model based on bidirectional temporal convolutional network and attention mechanism (BTCA) was proposed. Multi-layer dilated convolution was used to increase the receptive field and better capture long distance dependent information. At the same time, attention mechanism was used to increase the attention to the local key feature in the text, and the bidirectional temporal convolution network was used to extract contextual multi-scale semantic information to enrich semantics, the problem of sparse short text features was solved to a certain extent, and text classification effect was improved. The public corpus of the THUCNews was used to conduct a comparative experiment. It is pointed out that effect of short text classification is improved by using BTCA model, with an accuracy rate of 91.47%, which is better than commonly used models.