Text Classification Technology Research Articles

Research on text classification based on neural networks

With the rapid development of information technology, the processing of massive text data has become increasingly important. As a common computer information processing task, text classification has attracted a wide range of research interests. This paper aims to explore the text classification method based on neural network and analyze the key technologies. In order to solve the problem of text time series data classification, this paper uses the text time series data of occupancy detection and applies these neural network models in deep learning, including recurrent neural network, long short-term memory and gated recurrent unit, and trains the neural network through supervised learning. Inputting the room attribute data to these trained neural network models and judging the occupancy of the room. At the same time, observing the experimental results of these neural network models, including training loss, test loss and accuracy, to further study the performance of neural network in processing text time series data classification. This papers experiment aims to evaluate the performance of neural network in text classification and makes a detailed analysis through the experimental results. The goal of the research is to find an effective solution based on neural networks for the classification of text sequence data. Through the analysis of the experimental results, it can be concluded that the method based on neural network is feasible and effective in text sequence data classification. These analysis results will help to further promote the development of text classification technology and provide guidance and reference for practical application.

Transfer learning-based English translation text classification in a multimedia network environment.

In recent years, with the rapid development of the Internet and multimedia technology, English translation text classification has played an important role in various industries. However, English translation remains a complex and difficult problem. Seeking an efficient and accurate English translation method has become an urgent problem to be solved. The study first elucidated the possibility of the development of transfer learning technology in multimedia environments, which was recognized. Then, previous research on this issue, as well as the Bidirectional Encoder Representations from Transformers (BERT) model, the attention mechanism and bidirectional long short-term memory (Att-BILSTM) model, and the transfer learning based cross domain model (TLCM) and their theoretical foundations, were comprehensively explained. Through the application of transfer learning in multimedia network technology, we deconstructed and integrated these methods. A new text classification technology fusion model, the BATCL transfer learning model, has been established. We analyzed its requirements and label classification methods, proposed a data preprocessing method, and completed experiments to analyze different influencing factors. The research results indicate that the classification system obtained from the study has a similar trend to the BERT model at the macro level, and the classification method proposed in this study can surpass the BERT model by up to 28%. The classification accuracy of the Att-BILSTM model improves over time, but it does not exceed the classification accuracy of the method proposed in this study. This study not only helps to improve the accuracy of English translation, but also enhances the efficiency of machine learning algorithms, providing a new approach for solving English translation problems.

Chinese News Text Classification Method via Key Feature Enhancement

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

Text Classification Based on Machine Learning and Natural Language Processing Algorithms

Nowadays, with the development of media technology, people receive more and more information, but the current classification methods have the disadvantages of low classification efficiency and inability to identify multiple languages. In view of this, this paper is aimed at improving the text classification method by using machine learning and natural language processing technology. For text classification technology, this paper combines the technical requirements and application scenarios of text classification with ML to optimize the classification. For the application of natural language processing (NLP) technology in text classification, this paper puts forward the Trusted Platform Module (TPM) text classification algorithm. In the experiment of distinguishing spam from legitimate mail by text recognition, all performance indexes of the TPM algorithm are superior to other algorithms, and the accuracy of the TPM algorithm on different datasets is above 95%.

Towards Automated Screening of Literature on Artificial Intelligence in Nursing.

We evaluate the performance of multiple text classification methods used to automate the screening of article abstracts in terms of their relevance to a topic of interest. The aim is to develop a system that can be first trained on a set of manually screened article abstracts before using it to identify additional articles on the same topic. Here the focus is on articles related to the topic "artificial intelligence in nursing". Eight text classification methods are tested, as well as two simple ensemble systems. The results indicate that it is feasible to use text classification technology to support the manual screening process of article abstracts when conducting a literature review. The best results are achieved by an ensemble system, which achieves a F1-score of 0.41, with a sensitivity of 0.54 and a specificity of 0.96. Future work directions are discussed.

Statistical method of coal mine violations based on text classification technology

Statistical method of coal mine violations based on text classification technology

Construction of Conceptual Prospecting Model Based on Geological Big Data: A Case Study in Songtao-Huayuan Area, Hunan Province

With the era of big data, the prediction and evaluation of geological mineral resources have gradually entered into a new stage from digital prospecting to intelligent prospecting. The theoretical method of big data mining can contribute to deep mineral resource prediction and evaluation. This paper extracts ore-causing and ore-caused anomaly information based on text intelligent mining technology, and constructs a regional conceptual prospecting model based on geological prospecting big data. First, we set up a corpus based on text big data discovery and preprocessing technology. Second, we used CNN multiple scale text classification technology to analyze geological text data from the two main aspects: ore-causing anomalies and ore-caused anomalies. Third, we used a statistical method to analyze the semantic links between content-words, and we constructed chord diagrams and ternary diagrams to visualize the content-words and their links. Finally, we constructed a regional conceptual prospecting model based on the knowledge graphs.

Mining and Application of Tourism Online Review Text Based on Natural Language Processing and Text Classification Technology

This paper firstly describes the research status of online review text mining and finds out the problems existing in the mining and application of tourism texts. Aiming at these problems, this paper proposes a text mining method for tourism online reviews based on natural language processing and text classification technology. The first step is to analyze the validity of the online review text; the purpose is to remove the invalid text and improve the mining efficiency of the online review text. The second step is to conduct a comprehensive evaluation of scenic spots and hotels based on text classification technology and sentiment analysis. The comprehensive evaluation indicators are established for the five core service contents. High-quality scenic spots and hotels are selected according to the ranking of comprehensive evaluation. The third step is to propose a mining method of tourism hot words based on natural language processing for the selected high-quality tourist locations. The obtained hot words can intuitively show the impression of tourists on the scenic spot. The fourth step is to use mutual information combined with the left and right entropy to discover new words and to mine service characteristics of high-quality scenic spots and hotel from the new words. Finally, the proposed new methods are tested on the crawled tourism online review texts. The experimental results show that the novel comprehensive evaluation method proposed in this paper can truly and objectively select high-quality scenic spots and hotels and provide an important basis for the decision-making of tourism management. On this basis, hot words and new words can be effectively excavated from relevant online review texts, and travel impressions can be fed back from various aspects and angles.

Representation and association of Chinese financial equity knowledge driven by multilayer ontology

Aiming at the current situation of complex financial ownership structure and isolated data organization, this study referring to the methods for multi-layer hierarchical construct domain ontology modeling. At the same time, the three dimensions of industry, company and internal environment were integrated, and the concept cube was designed and constructed based on knowledge extraction and text classification technology, so as to provide a multi-level and fine-grained knowledge representation and association method for financial equity knowledge. The experimental results show that conceptual cube structure represents semantic information as a dense low-dimensional representation vector, which greatly enhances semantic relevance and interpretability. The multi-layer ontology-driven ownership structure reflects a variety of knowledge association patterns, and in the “Intelligent Financial Big Data System” developed by the research team, the association query of three categories of association relationships in the field of industry, enterprise and internal environment is realized, as well as the dynamic analysis and supervision of typical financial management problems.

Case Characterization and Discipline Measurement Method Based on Discipline Inspection and Supervision Knowledge Graph

As an important link to realize intelligent discipline inspection and supervision, the term “case characterization and discipline measurement” refers to the automatic extraction of material facts from case description and the conclusion of conformity and nonconformity after comparison in accordance with legal norms. In response to the problem that there was no special method for the task of case characterization and discipline measurement, the paper combined the practical case handling process of the staff and proposed a method of case characterization and discipline measurement based on discipline inspection and supervision knowledge graph. The method uses the knowledge graph as auxiliary information and aligns the entities of regulations and cases using knowledge fusion technology to construct the discipline inspection and supervision knowledge graph. For the newborn case descriptions, named entity recognition technology is used to extract the key elements that determine the verdict outcome. Similar cases were identified with the same discipline breach nature. Then, text classification technology is used to predict the severity of case circumstances. Combined with the disciplinary violation facts, the disciplinary result is given according to the party discipline rules. Experiments were carried out with a dataset of typical cases notified by the discipline inspection and supervision. According to the experimental results, the proposed method shows its validity, which improves the interpretability of case characterization and discipline measurement and fills the field gap.

МОДЕЛІ ГЛИБОКОГО НАВЧАННЯ ДЛЯ ВИРІШЕННЯ ЗАДАЧІ КЛАСИФІКАЦІЇ ТЕКСТОВОЇ ІНФОРМАЦІЇ

Text analysis as a whole is a new field of study. Fields such as marketing, product management, research, and management already use the process of analysing and extracting information from textual data. In the previous post, we discussed text classification technology, one of the most important parts of text analysis. Text classification or text categorisation is the activity of labelling texts in natural language with appropriate categories from a predetermined set. To put it bluntly, text classification is the process of extracting generic tags from unstructured text. These generic tags come from a set of predefined categories. Categorising content and products helps users easily find and navigate to a website or app. Text classification, also known as text categorisation, is a classic problem in natural language processing (NLP) that aims to assign labels or tags to text units such as sentences, queries, paragraphs, and documents. It has a wide range of applications, including question answering, spam detection, sentiment analysis, news categorisation, user intent classification, content moderation, and more. Text data can come from a variety of sources, including web data, emails, chats, social media, tickets, insurance claims, user feedback, and customer service questions and answers. The text is an extremely rich source of information. But extracting useful data from text is usually difficult and time-consuming due to the unstructured nature of natural language information. Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorisation, question answering, and natural language inference. In this paper, we provide a comprehensive review of most widespread deep learning based models for text classification developed in recent years, and discuss their technical contributions, similarities, and strengths.

Prediction Model of Juvenile Football Players’ Sports Injury Based on Text Classification Technology of Machine Learning

As the level of soccer in our country has improved rapidly, the level of skill has gradually improved, and the requirements for training of athletes have increased. Due to changes in athlete training methods, it has been decided that athletes must bear a great risk of sports injuries. Accurate prediction of injuries is very important for the development of youth soccer. Based on this, this paper proposes a text classification algorithm based on machine learning and builds a sports injury prediction model that can accurately predict athlete injuries, reduce athlete injuries during training, and be effective. We put forward various sports suitable for young athletes, and put forward some measures to prevent and alleviate athletes’ injuries. This article selects 48 football players from a college of physical education of a university for testing. The athletes participating in the experiment use professional equipment to collect exercise volume and exercise load data, and real-time records of each athlete's physical fitness data within half a year, through the athlete's exercise volume, exercise load, body metabolism, and physical indicators to predict their sports injury. Experiments show that from the degree of injury, it can be seen that the severe injury is the least, with 5 cases of muscle injury, 2 cases of fascia ligament injury, and 1 case of joint injury. There were 25 cases of mild injuries, accounting for 41.0% of the total. This is because the athlete’s sports injury prediction model has better prediction capabilities, allowing athlete coaches and therapists to optimize training courses, ultimately preventing injuries, improving training levels, and reducing rehabilitation costs.

Text Classification Using Novel Term Weighting Scheme-Based Improved TF-IDF for Internet Media Reports

With the rapid development of the internet technology, a large amount of internet text data can be obtained. The text classification (TC) technology plays a very important role in processing massive text data, but the accuracy of classification is directly affected by the performance of term weighting in TC. Due to the original design of information retrieval (IR), term frequency-inverse document frequency (TF-IDF) is not effective enough for TC, especially for processing text data with unbalanced distributions in internet media reports. Therefore, the variance between the DF value of a particular term and the average of all DFsDF¯, namely, the document frequency variance (ADF), is proposed to enhance the ability in processing text data with unbalanced distribution. Then, the normal TF-IDF is modified by the proposed ADF for processing unbalanced text collection in four different ways, namely, TF-IADF, TF-IADF+, TF-IADFnorm, and TF-IADF+norm. As a result, an effective model can be established for the TC task of internet media reports. A series of simulations have been carried out to evaluate the performance of the proposed methods. Compared with TF-IDF on state-of-the-art classification algorithms, the effectiveness and feasibility of the proposed methods are confirmed by simulation results.

Design of Intelligent Customer Service Report System Based on Automatic Speech Recognition and Text Classification

In combination with features such as intensive labor and speech in the customer service report field, this paper discusses the design of a customer service report system based on artificial intelligence automatic speech recognition technology and big data text classification technology. The proposed system realizes functions like a flat IVR menu, quick transcription and input of work orders, dynamic tracking of failure hotspots, automatic classification and accumulation of the knowledge base, speech emotion detection and real-time supervision of service quality, and it can improve the user experience and reduce the labor strengths of customer service staff. The automatically accumulated knowledge base can further assist with feedback to resolve the difficult problem that the emerging intelligent network Q&A and intelligent robots rely on a manually summarized knowledge base.

Research on the Performance of Character Convolutional Neural Network in Different Text Encoding Formats

With the continuous progress and development of society, text classification has become an important task in the field of text data mining and text value exploration. Compared with existing text classification technology, deep learning technology has many advantages, such as high accuracy and effective feature extraction. This paper is mainly based on the character convolutional neural network to study the classification performance of different texts, hoping to provide experience for related research work. Through research, it is found that character-level convolutional neural network deep learning technology can achieve Chinese text classification more effectively.

Fusing Logical Relationship Information of Text in Neural Network for Text Classification

With the development of computer science and information science, text classification technology has been greatly developed and its application scenarios have been widened. In traditional process of text classification, the existing method will lose much logical relationship information of text. The logical relationship information of a text refers to the relationship information among different logical parts of the text, such as title, abstract, and body. When human beings are reading, they will take title as an important part to remind the central idea of the article, abstract as a brief summary of the content of the article, and body as a detailed description of the article. In most of the text classification studies, researchers concern more about the relationship among words (word frequency, semantics, etc.) and neglect the logical relationship information of text. It will lose information about the relationship among different parts (title, body, etc.) and have an influence on the performance of text classification. Therefore, we propose a text classification algorithm—fusing the logical relationship information of text in neural network (FLRIOTINN), which complements the logical relationship information into text classification algorithms. Experiments show that the effect of FLRIOTINN is better than the conventional backpropagation neural networks which does not consider the logical relationship information of text.

An Optimized Mining Algorithm for Analyzing Students’ Learning Degree Based on Dynamic Data

With the rapid development of educational informatization, it has enabled education to enter the era of big data. How to extract effective information from educational big data and realize adaptive personalized learning goals have become the current research hotspot. The traditional static data only analyzes the students' learning degree based on the students' final answer, but ignores the dynamic data in the process of answering questions, such as the modification and the time it answered on the question, which makes it difficult to fully and accurately mine the correlation between the massive data, so it turns from static data mining to dynamic data mining. The paper proposes an optimized mining algorithm for analyzing students' learning degree based on dynamic data. The algorithm first uses the optimized text classification technology to match the question texts to the knowledge points automatically, so as to improves the efficiency and quality. Then, it uses the subjective weighting method combined with the expert experience to generate the learning degree matrix of students on knowledge points based on dynamic data of the students' records. Finally, the DBSCAN clustering algorithm is used to cluster the personalized learning characteristics of students according to the learning degree matrix. The experimental result shows that the algorithm can deal with massive data automatically and effectively, and analyze the students' learning degree on knowledge points comprehensively and accurately, so as to classify students and realize personalized teaching.

Semi-supervised learning in large scale text categorization

The rapid development of the Internet brings a variety of original information including text information, audio information, etc. However, it is difficult to find the most useful knowledge rapidly and accurately because of its huge number. Automatic text classification technology based on machine learning can classify a large number of natural language documents into the corresponding subject categories according to its correct semantics. It is helpful to grasp the text information directly. By learning from a set of hand-labeled documents, we obtain the traditional supervised classifier for text categorization (TC). However, labeling all data by human is labor intensive and time consuming. To solve this problem, some scholars proposed a semi-supervised learning method to train classifier, but it is unfeasible for various kinds and great number of Web data since it still needs a part of hand-labeled data. In 2012, Li et al. invented a fully automatic categorization approach for text (FACT) based on supervised learning, where no manual labeling efforts are required. But automatically labeling all data can bring noise into experiment and cause the fact that the result cannot meet the accuracy requirement. We put forward a new idea that part of data with high accuracy can be automatically tagged based on the semantic of category name, then a semi-supervised way is taken to train classifier with both labeled and unlabeled data, and ultimately a precise classification of massive text data can be achieved. The empirical experiments show that the method outperforms the supervised support vector machine (SVM) in terms of both F1 performance and classification accuracy in most cases. It proves the effectiveness of the semi-supervised algorithm in automatic TC.

A Method of Web Page Classification Based on Feature Dimension Reduction

Text classification technology to quickly retrieve information pages, information filtering and data mining provides an important foundation. Due to the diversity and complexity of web page format, the problem of web page classification is more difficult to deal with than text classification. This paper on web page classification method research, through the improvement of bloom filter, after pretreatment of the text content of pages, the improved bloom filter used in filtering feature set, greatly reducing the feature dimension, then according to the characteristics of web page, the feature weight algorithm has been improved. Finally, using the naive Bayesian classifier can verify the effectiveness of the algorithm.

Turning from TF-IDF to TF-IGM for term weighting in text classification

Massive textual data management and mining usually rely on automatic text classification technology. Term weighting is a basic problem in text classification and directly affects the classification accuracy. Since the traditional TF-IDF (term frequency & inverse document frequency) is not fully effective for text classification, various alternatives have been proposed by researchers. In this paper we make comparative studies on different term weighting schemes and propose a new term weighting scheme, TF-IGM (term frequency & inverse gravity moment), as well as its variants. TF-IGM incorporates a new statistical model to precisely measure the class distinguishing power of a term. Particularly, it makes full use of the fine-grained term distribution across different classes of text. The effectiveness of TF-IGM is validated by extensive experiments of text classification using SVM (support vector machine) and kNN (k nearest neighbors) classifiers on three commonly used corpora. The experimental results show that TF-IGM outperforms the famous TF-IDF and the state-of-the-art supervised term weighting schemes. In addition, some new findings different from previous studies are obtained and analyzed in depth in the paper.