Accuracy Of Text Classification Research Articles

AraXLNet: pre-trained language model for sentiment analysis of Arabic

The Arabic language is a complex language with little resources; therefore, its limitations create a challenge to produce accurate text classification tasks such as sentiment analysis. The main goal of sentiment analysis is to determine the overall orientation of a given text in terms of whether it is positive, negative, or neutral. Recently, language models have shown great results in promoting the accuracy of text classification in English. The models are pre-trained on a large dataset and then fine-tuned on the downstream tasks. Particularly, XLNet has achieved state-of-the-art results for diverse natural language processing (NLP) tasks in English. In this paper, we hypothesize that such parallel success can be achieved in Arabic. The paper aims to support this hypothesis by producing the first XLNet-based language model in Arabic called AraXLNet, demonstrating its use in Arabic sentiment analysis in order to improve the prediction accuracy of such tasks. The results showed that the proposed model, AraXLNet, with Farasa segmenter achieved an accuracy results of 94.78%, 93.01%, and 85.77% in sentiment analysis task for Arabic using multiple benchmark datasets. This result outperformed AraBERT that obtained 84.65%, 92.13%, and 85.05% on the same datasets, respectively. The improved accuracy of the proposed model was evident using multiple benchmark datasets, thus offering promising advancement in the Arabic text classification tasks.

The Construction of Intelligent Emotional Analysis and Marketing Model of B&B Tourism Consumption Under the Perspective of Behavioral Psychology.

This manuscript constructs an intelligent sentiment analysis and marketing model for bed and breakfast (B&B) consumption based on a behavioral psychology perspective. Based on the LDA theme model, the theme features and keywords of the reviews covering user feedback are explored from the text data, and the theme framework of user sentiment perception is constructed by combining previous literature on user perception in the B&B market, and the themes of user online reviews are summarized in four dimensions: practical, sensory, cognitive, and emotional components of user experience. In this manuscript, GooSeeker software was selected for data crawling and ROST CM (ROST content mining) developed by Wuhan University was used for text processing. To improve the accuracy of text classification and improve the missing data, the online comment text is divided into sentences by symbols, and the text is divided into words based on sentences, and the spatial vector model and the text feature word weighting method of TF-IDF are used for vector representation, and the polynomial Bayesian classifier is called to identify the topics of sentences. The classical Theory of Planned Behavior (TPB) was used to analyze the influencing factors of the willingness to consume experiential B&B tourism, and countermeasure suggestions for the development of B&B tourism were proposed based on the research findings In the empirical testing stage, a questionnaire on the willingness to consume experiential B&B tourism was designed, and web research was chosen to collect the data. SPSS20.0 was used to conduct reliability analysis, factor analysis, correlation analysis, and regression analysis on the data, and AMOS statistics were used to establish a structural equation model to verify the influence path of willingness to consume experiential B&B tourism. Finally, the moderating path of willingness to consume experiential B&B tourism was verified by using multi-group analysis.

Short-Text Classification Detector: A Bert-Based Mental Approach.

With the continuous development of the Internet, social media based on short text has become popular. However, the sparsity and shortness of essays will restrict the accuracy of text classification. Therefore, based on the Bert model, we capture the mental feature of reviewers and apply them for short text classification to improve its classification accuracy. Specifically, we construct a model text at the language level and fine tune the model to better embed mental features. To verify the accuracy of this method, we compare a variety of machine learning methods, such as support vector machine, convolution neural networks, and recurrent neural networks. The results show the following: (1) Through feature comparison, it is found that mental features can significantly improve the accuracy of short text classification. (2) Combining mental features and text as input vectors can provide more classification accuracy than separating them as two independent vectors. (3) Through model comparison, it can be found that Bert model can integrate mental features and short text. Bert can better capture mental features to improve the accuracy of classification results. This will help to promote the development of short text classification.

Improving Arabic Text Classification Using P-Stemmer

Introduction: Stemming is an important preprocessing step in text classification, and could contribute in increasing text classification accuracy. Although many works proposed stemmers for English language, few stemmers were proposed for Arabic text. Arabic language has gained increasing attention in the previous decades and the need is vital to further improve Arabic text classification. Method: This work combined the use of the recently proposed P-Stemmer with various classifiers to find the optimal classifier for the P-stemmer in term of Arabic text classification. As part of this work, a synthesized dataset was collected. Result: The previous experiments show that the use of P-Stemmer has a positive effect on classification. The degree of improvement was classifier-dependent, which is reasonable as classifiers vary in their methodologies. Moreover, the experiments show that the best classifier with the P-Stemmer was NB. This is an interesting result as this classifier is wellknown for its fast learning and classification time. Discussion: First, the continuous improvement of the P-Stemmer by more optimization steps is necessary to further improve the Arabic text categorization. This can be made by combining more classifiers with the stemmer, by optimizing the other natural language processing steps, and by improving the set of stemming rules. Second, the lack of sufficient Arabic datasets, especially large ones, is still an issue. Conclusion: In this work, an improved P-Stemmer was proposed by combining its use with various classifiers. In order to evaluate its performance, and due to the lack of Arabic datasets, a novel Arabic dataset was synthesized from various online news pages. Next, the P-Stemmer was combined with Naïve Bayes, Random Forest, Support Vector Machines, KNearest Neighbor, and K-Star.

Text Data Processing and Classification Algorithm Based on Data Fusion and Granular Computing

With the rapid expansion of network information and the emergence of a large number of electronic texts, how to organize and manage this massive information has become a major challenge. Automatic text categorization technology is to study how to let the machine classify unknown text through self-learning, thus solving the difficulties encountered in manual classification. Because granular computing can reduce the knowledge in solving complex problems, it is more convenient to summarize and acquire knowledge. It has become a hotspot in recent years, and it also provides new ideas for text classification research. The rough set model of granular computing can acquire knowledge by mining decision rules. The decision process is more transparent and easy to understand. It has been paid attention to and applied in text classification research. Based on the research of existing achievements, this paper makes a further study on the application of granular computing in text categorization. After analyzing the existing feature selection methods, the feature distribution is proposed based on the relationship between feature words and categories. By calculating the distribution distance between any two feature words, the feature words with similar distribution distances are aggregated, which effectively reduces the dimension of the feature space and also avoids the individual samples caused by the existing feature selection algorithm. A phenomenon that is discarded due to features. The experimental results show that the clustering method can obtain higher classification accuracy than other feature selection methods when using SVM as the classifier. SVM performs best, and the final text classification accuracy rate can reach 85.46%. According to the correlation principle of the rough set, feature selection is made for each information granularity, the selected feature is used as the condition attribute and the coordination matrix is constructed, and the most similar sample is heuristically searched to obtain the attribute reduction set.

A Long-Text Classification Method of Chinese News Based on BERT and CNN

Text Classification is an important research area in natural language processing (NLP) that has received a considerable amount of scholarly attention in recent years. However, real Chinese online news is characterized by long text, a large amount of information and complex structure, which also reduces the accuracy of Chinese long text classification as a result. To improve the accuracy of long text classification of Chinese news, we propose a BERT-based local feature convolutional network (LFCN) model including four novel modules. First, to address the limitation of Bidirectional Encoder Representations from Transformers (BERT) on the length of the max input sequence, we propose a named Dynamic LEAD-n (DLn) method to extract short texts within the long text based on the traditional LEAD digest algorithm. In Text-Text Encoder (TTE) module, we use BERT pretrained language model to complete the sentence-level feature vector representation of a news text and to capture global features by using the attention mechanism to identify correlated words in text. After that, we propose a CNN-based local feature convolution (LFC) module to capture local features in text, such as key phrases. Finally, the feature vectors generated by the different operations over several different periods are fused and used to predict the category of a news text. Experimental results show that the new method further improves the accuracy of long text classification of Chinese news.

Application of deep learning algorithms detecting fake and correct textual or verbal news

Abstract. The ongoing spread and expansion of information technology and social media sites have made it easier for people to access different types of news – political, economic, medical, social, etc. through these platforms. However, this rapid growth in news outlets and the demand for information has blurred the lines between real and fake news and led to the dissemination of fake news, which is a dangerous state of affairs. The outbreak of the coronavirus pandemic and awareness of the threats posed all across the globe. And a parallel rise in fake news and rumors, like unsubstantiated statements and deceptive ideas, were noticed. The main aim of this study is supposed to set out to overcome these kinds of problems in the future with the application of deep learning algorithms (LSTM, Bi-LSTM, BERT), using a large dataset (39279 rows) to identify fake and correct textual or verbal news. The results of the deep learning application using different algorithms show that the BERT model performed the best, achieving a text classification accuracy of 96.63 %.

CNO-LSTM: A Chaotic Neural Oscillatory Long Short-Term Memory Model for Text Classification

Long Short-Term Memory (LSTM) networks are unique to exercise data in its memory cell with long-term memory as Natural Language Processing (NLP) tasks have inklings of intensive time and computational power due to their complex structures like magnitude language model Transformer required to pre-train and learn billions of data performing different NLP tasks. In this paper, a dynamic chaotic model is proposed for the objective of transforming neurons states in network with neural dynamic characteristics by restructuring LSTM as Chaotic Neural Oscillatory-Long-Short Term Memory (CNO-LSTM), where neurons in LSTM memory cells are weighed in substitutes by oscillatory neurons to speed up computational training of language model and improve text classification accuracy for real-world applications. From the implementation perspective, five popular datasets of general text classification including binary, multi classification and multi-label classification are used to compare with mainstream baseline models on NLP tasks. Results showed that the performance of CNO-LSTM, a simplified model structure and oscillatory neurons state in exercising different types of text classification tasks are above baseline models in terms of evaluation index such as Accuracy, Precision, Recall and F1. The main contributions are time reduction and improved accuracy. It achieved approximately 46.76% of the highest reduction training time and 2.55% accuracy compared with vanilla LSTM model. Further, it achieved approximately 35.86% in time reduction compared with attention model without oscillatory indicating that the model restructure has reduced GPU dependency to improve training accuracy.

Features Optimization of Gray Level Co-Occurrence Matrix by Artificial Bee Colony Algorithm for Texture Classification

Gray Level Co-occurrence Matrix (GLCM) is one of the most popular texture analysis methods. The fundamental issue of GLCM is the suitable selection of input parameters, where many researchers depended on trial and observation approach for selecting the best combination of GLCM parameters to improve the texture classification, which is tedious and time-consuming. This paper proposes a new optimization method for the GLCM parameters using Artificial Bee Colony Algorithm (ABC) to improve the binary texture classification. For the testing, 13 Haralick features were extracted from the UMD database, which has been used with the multi-layer perceptron neural network classifier. The experimental results proved that, the proposed method has been succeeded to finding the best combination of GLCM parameters that leads to the best binary texture classification accuracy performance.

Emotionally charged text classification with deep learning and sentiment semantic

Text classification is one of the widely used phenomena in different natural language processing tasks. State-of-the-art text classifiers use the vector space model for extracting features. Recent progress in deep models, recurrent neural networks those preserve the positional relationship among words achieve a higher accuracy. To push text classification accuracy even higher, multi-dimensional document representation, such as vector sequences or matrices combined with document sentiment, should be explored. In this paper, we show that documents can be represented as a sequence of vectors carrying semantic meaning and classified using a recurrent neural network that recognizes long-range relationships. We show that in this representation, additional sentiment vectors can be easily attached as a fully connected layer to the word vectors to further improve classification accuracy. On the UCI sentiment labelled dataset, using the sequence of vectors alone achieved an accuracy of 85.6%, which is better than 80.7% from ridge regression classifier—the best among the classical technique we tested. Additional sentiment information further increases accuracy to 86.3%. On our suicide notes dataset, the best classical technique—the Naíve Bayes Bernoulli classifier, achieves accuracy of 71.3%, while our classifier, incorporating semantic and sentiment information, exceeds that at 75% accuracy.

Hybrid embedding-based text representation for hierarchical multi-label text classification

Many real-world text classification tasks often deal with a large number of closely related categories organized in a hierarchical structure or taxonomy. Hierarchical multi-label text classification (HMTC) has become rather challenging when it requires handling large sets of closely related categories. The structural features of all categories in the entire hierarchy and the word semantics of their category labels are very helpful in improving text classification accuracy over large sets of closely related categories, which has been neglected in most of existing HMTC approaches. In this paper, we present a hybrid embedding-based text representation for HMTC with high accuracy. First, the hybrid embedding consists of both graph embedding of categories in the hierarchy and their word embedding of category labels. The Structural Deep Network Embedding-based graph embedding model is used to simultaneously encode the global and local structural features of a given category in the whole hierarchy for making the category structurally discriminable. We further use the word embedding technique to encode the word semantics of each category label in the hierarchy for making different categories semantically discriminable. Second, we presented a level-by-level HMTC approach based on the bidirectional Gated Recurrent Unit network model together with the hybrid embedding that is used to learn the representation of the text level-by-level. Last but not least, extensive experiments were made over five large-scale real-world datasets in comparison with the state-of-the-art hierarchical and flat multi-label text classification approaches, and the experimental results show that our approach is very competitive to the state-of-the-art approaches in classification accuracy, in particular maintaining computational costs while achieving superior performance.

CrowdTC: Crowd-powered Learning for Text Classification

Text classification is a fundamental task in content analysis. Nowadays, deep learning has demonstrated promising performance in text classification compared with shallow models. However, almost all the existing models do not take advantage of the wisdom of human beings to help text classification. Human beings are more intelligent and capable than machine learning models in terms of understanding and capturing the implicit semantic information from text. In this article, we try to take guidance from human beings to classify text. We propose Crowd-powered learning for Text Classification (CrowdTC for short). We design and post the questions on a crowdsourcing platform to extract keywords in text. Sampling and clustering techniques are utilized to reduce the cost of crowdsourcing. Also, we present an attention-based neural network and a hybrid neural network to incorporate the extracted keywords as human guidance into deep neural networks. Extensive experiments on public datasets confirm that CrowdTC improves the text classification accuracy of neural networks by using the crowd-powered keyword guidance.

An Enhanced Gated Recurrent Unit with Auto-Encoder for Solving Text Classification Problems

Classification has become an important task for automatically categorizing documents based on their respective group. The purpose of classification is to assign the pre-specified group or class to an instance based on the observed features related to that instance. For accurate text classification, feature selection techniques are normally used to identify important features and to remove irrelevant, undesired and noisy features for minimizing the dimensionality of feature space. Therefore, in this research, a new model namely Encoder Simplified GRU (ES-GRU) is proposed to reduce dimension of data using an auto-encoder (AE). Gated Recurrent Unit (GRU) is a deep learning algorithm that contains update gate and reset gate, which is considered as one of the most efficient text classification technique, specifically on sequential datasets. Accordingly, the reset gate is replaced with an update gate in order to reduce the redundancy and complexity in the standard GRU. The proposed model has been evaluated on five benchmark text datasets and compared with six baseline well-known text classification approaches, which includes standard GRU, AE, Long Short-Term Memory, Convolutional Neural Network, Support Vector Machine, and Naïve Bayes. Based on various types of performance evaluation parameters, a considerable amount of improvement has been observed in the performance of the proposed model as compared to state-of-the-art approaches.

Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification

Recent studies have shown that cyberbullying is a rising youth epidemic. In this paper, we develop a novel automated classification model that identifies the cyberbullying texts without fitting them into large dimensional space. On the other hand, a classifier .cannot provide a limited convergent solution due to its overfitting problem. Considering such limitations, we developed a text classification engine that initially pre-processes the tweets, eliminates noise and other background information, extracts the selected features and classifies without data overfitting. The study develops a novel Deep Decision Tree classifier that utilizes the hidden layers of Deep Neural Network (DNN) as its tree node to process the input elements. The validation confirms the accuracy of classification using the novel Deep classifier with its improved text classification accuracy.

Discussion on Text Classification Algorithm Based on Genetic Algorithm and Probability Theory

This article mainly studies the text classification algorithm based on genetic algorithm and probability theory, and improves the speed and accuracy of text classification by using the related knowledge of genetic algorithm and probability theory. Preliminary assignment of feature items is carried out through TF algorithm, and then special non-substantial words are shielded. Using L-E operator for weighting calculation can make the better results converge faster. Using genetic algorithm, using crossover operator, mutation operator and establishing a suitable objective function, speed up the retrieval speed and improve the efficiency of obtaining the best results. Using a hybrid algorithm can eliminate the interference of synonyms and non-characteristics.

Research on human-vehicle interaction based on natural language processing

With the rapid development of intelligent networked vehicles and driverless technology, the importance of the dialogue between human and vehicle artificial intelligence has also become prominent. In the case of the driver outputting speech and converting it into text, how to correctly distinguish the driver’s intention and make the correct response is the purpose of the research. Google released the first-generation BERT (Bidirectional Encoder Representations from Transformers) training model in 2018, bringing natural language processing methods to a new way of understanding. This article analyzes the current mainstream machine learning algorithms and applies them to natural language processing (NLP) to classify text data sets. The purpose is to study various machine learning models to achieve higher accuracy of text classification. Research results not only lay the foundation for improving vehicle driver interaction, but also serve as a reference for the natural language processing part of the future car networking and driverless technology.

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.

Automated classification of BI-RADS in textual mammography reports

The main purpose of this paper is to process key information in medical text records and also classifypatients, per different levels of breast imaging-reporting and data system (BI-RADS). The BI-RADS is a scheme for thestandardization of breast imaging reports. Therefore, medical text mining is employed to classify mammography reportssupported BI-RADS. In this research, a new method is proposed for automated BI-RADS classifications extraction fromtextual reports and improves the therapeutic procedures. At first, a mammography lexicon is employed for choosingkeywords from medical text reports. Word2vec and term frequency inverse document frequency (TFIDF) techniques areused for extracting features, finally, they are combined with the hospital information system (HIS) reports and calledWith-HIS. The different classifiers like multiclass support vector machine (SVM), naïve Bayesian (NB), extreme gradientboosting (XGBoost), and multilevel fuzzy min-max neural network (MLF) are used so as to compare the accuracy ofWith-HIS and without HIS (called Without-HIS). The results are confirmed that using HIS beside the proposed approach(Word2vec +TFIDF) encompasses a significant effect on the accuracy of medical text classification. Accuracy within theproposed method with MLF classifier is 0.89% but Without-HIS is 0.85%.

A Weakly Supervised and Deep Learning Method for an Additive Topic Analysis of Large Corpora

Abstract The collaborative effort of theory-driven content analysis can benefit significantly from the use of topic analysis methods, which allow researchers to add more categories while developing or testing a theory. This additive approach enables the reuse of previous efforts of analysis or even the merging of separate research projects, thereby making these methods more accessible and increasing the discipline’s ability to create and share content analysis capabilities. This paper proposes a weakly supervised topic analysis method that uses both a low-cost unsupervised method to compile a training set and supervised deep learning as an additive and accurate text classification method. We test the validity of the method, specifically its additivity, by comparing the results of the method after adding 200 categories to an initial number of 450. We show that the suggested method provides a foundation for a low-cost solution for large-scale topic analysis.

Novel Efficient RNN and LSTM-Like Architectures: Recurrent and Gated Broad Learning Systems and Their Applications for Text Classification.

High accuracy of text classification can be achieved through simultaneous learning of multiple information, such as sequence information and word importance. In this article, a kind of flat neural networks called the broad learning system (BLS) is employed to derive two novel learning methods for text classification, including recurrent BLS (R-BLS) and long short-term memory (LSTM)-like architecture: gated BLS (G-BLS). The proposed two methods possess three advantages: 1) higher accuracy due to the simultaneous learning of multiple information, even compared to deep LSTM that extracts deeper but single information only; 2) significantly faster training time due to the noniterative learning in BLS, compared to LSTM; and 3) easy integration with other discriminant information for further improvement. The proposed methods have been evaluated over 13 real-world datasets from various types of text classification. From the experimental results, the proposed methods achieve higher accuracies than LSTM while taking significantly less training time on most evaluated datasets, especially when the LSTM is in deep architecture. Compared to R-BLS, G-BLS has an extra forget gate to control the flow of information (similar to LSTM) to further improve the accuracy on text classification so that G-BLS is more effective while R-BLS is more efficient.