Text Classification Algorithms Research Articles

Improving Probabilistic Models In Text Classification Via Active Learning

Social scientists often classify text documents to use the resulting labels as an outcome or a predictor in empirical research. Automated text classification has become a standard tool since it requires less human coding. However, scholars still need many human-labeled documents for training. To reduce labeling costs, we propose a new algorithm for text classification that combines a probabilistic model with active learning. The probabilistic model uses both labeled and unlabeled data, and active learning concentrates labeling efforts on difficult documents to classify. Our validation study shows that with few labeled data, the classification performance of our algorithm is comparable to state-of-the-art methods at a fraction of the computational cost. We replicate the results of two published articles with only a small fraction of the original labeled data used in those studies and provide open-source software to implement our method.

Analisis Sentimen Komentar Video Putusan MA Terkait Kaesang Menggunakan Metode Naive Bayes

Political dynasty is a political power exercised by a group of people who are related by family, with the aim of obtaining power and ensuring that this power remains within the group by passing it on to other family members. This study conducts a sentiment analysis on comments related to the Supreme Court decision which is believed to pave the way for Kaesang Pangarep in support of Jokowi's political dynasty. Sentiment analysis is carried out using the Naive Bayes method, a commonly used algorithm for text classification based on probability. The data used consists of comments from videos taken from social media platforms. These comments are then categorized into positive, negative, and neutral sentiments. The results of the study show the distribution of public sentiment towards this issue, providing an overview of how the public responds to the decision. The Naive Bayes method is chosen for its simplicity and its ability to provide reasonably accurate results in text analysis.

Do Algorithms Dream of Electric Requirements? Leveraging AI‐Based Approaches for Automated Allocation and Classification of Requirements in Railway Engineering

AbstractIn recent years, Artificial Intelligence has experienced an extraordinary growth. Systems Engineering is a discipline where the implementation of AI can be challenging, but that could immensely benefit from its capabilities. This paper presents one of the many implementations that AI can have within the Systems Engineering field. AI has been leveraged to create an algorithm that allows for the automatic identification and classification of requirements within a specific engineering sector: large railway projects. While text classification algorithms are well established, the key to a successful implementation of a requirements classification algorithm lays on the effective structurization of the data, as well as the high quality of the training datasets. This paper describes how an AI‐based requirements classification algorithm has been planned and trained to effectively classify requirements in future documents based on systems and subsystems from a System Breakdown Structure (SBS), as well as to predict the adequate method of verification for both the Design and Testing and Commissioning stages of a railway project.Finally, the paper showcases how the use of this AI‐based requirements classifier does not only lower the probability of human error, but also reduces ∼75% human workload per project. Additionally, overall ∼30% cost savings to organizations are expected in a 10‐year period in the task of classifying requirements with respect to manual classification performed by subject matter experts.

Evaluating Health Services and Operations among Barangay Health Offices: Basis for Document Digitalization with Text Classification Algorithm and Machine Learning

Evaluating Health Services and Operations among Barangay Health Offices: Basis for Document Digitalization with Text Classification Algorithm and Machine Learning

Automatic Content Analysis Systems: Detecting Disinformation in Social Networks

In the 21st century, the rapid rise of disinformation and propaganda has become a significant global issue, undermining democratic processes and socio-political institutions. Disinformation, defined as intentionally false or misleading information, aims to manipulate public opinion and cause economic harm. This paper explores the use of computational linguistics and machine learning methods to detect disinformation. Techniques such as text preprocessing, feature extraction, and classification algorithms (e.g., SVM, naive Bayes) are adapted for identifying fake news. Recent studies demonstrate the effectiveness of these methods in social media and news platforms, highlighting the importance of advanced models like GPT-4 in improving detection accuracy and combating the spread of disinformation.

Comparing text classification algorithms with n-grams for mediation prediction

Comparing text classification algorithms with n-grams for mediation prediction

Generic Framework for Vehicle Identification System with Deep Learning Models

In contemporary vehicle scenario, different kinds of vehicles are playing vital role for the customers. Day by day, vehicles are increasing with different properties and identifications. Monitoring and maintain the records of vehicles in digital platform is the challenging task as well as a crucial task for the countries. Here, we have to take the scenario of digitalization of vehicles with deep learning network models for identification and maneuvering process. In our country, different states are following multiple vehicle identification like numbering, coloring and imaging. Many researchers were contributing towards this identification mechanism like supervised, unsupervised models for getting optimum accuracy. Text and Image classification are not sufficient to solve this vehicle identification problem. We are incorporating text, image and video related unstructured data set to solve this problem. We have framed a generic architecture for all kind of vehicles analyze the data. For instance, a vehicle is moving towards CCTV camera and it has been recorded as video content. In this paper, we come across video to still pictures and image segmentation and all the identification parameters like White, Yellow and green. We performed both text classification and image classification algorithms to acquire effective result compared to existing algorithms. Bi Directional RNN (LSTM/GRU) and LeNet, Alex Net, Inception, VggNet and ResNet algorithms are analyzed for this entire process. This is one of the globally challenging scenarios for identification with feature extraction, data cleaning and processing. At the end of the result, we achieved 98.94 % accuracy compare than other existing systems.

Text Classification and Cluster Analysis based on Deep Learning and Natural Language Processing

At present, the commonly used Bag of Words (BOW) expression ignores the semantic information of text and the problems of high dimension and high sparsity of feature extraction. This paper presents a multi-class text representation and classification algorithm. This project is based on the vector expression of keywords and takes the multi-category classification problem as the research object. Then, a hybrid Deep Location network (HDBN) is constructed by combining DBN with Boltzmann (DBM). Then, this paper does a lot of tests on the algorithm and proves the effectiveness of the algorithm. In addition, the 2D visual experiment is carried out with HDBN, and then the high-level text expression based on HDBN is obtained. The expression has strong cohesion and weak coupling.

Novel GCN Model Using Dense Connection and Attention Mechanism for Text Classification

Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) based text classification algorithms currently in use can successfully extract local textual features but disregard global data. Due to its ability to understand complex text structures and maintain global information, Graph Neural Network (GNN) has demonstrated considerable promise in text classification. However, most of the GNN text classification models in use presently are typically shallow, unable to capture long-distance node information and reflect the various scale features of the text (such as words, phrases, etc.). All of which will negatively impact the performance of the final classification. A novel Graph Convolutional Neural Network (GCN) with dense connections and an attention mechanism for text classification is proposed to address these constraints. By increasing the depth of GCN, the densely connected graph convolutional network (DC-GCN) gathers information about distant nodes. The DC-GCN multiplexes the small-scale features of shallow layers and produces different scale features through dense connections. To combine features and determine their relative importance, an attention mechanism is finally added. Experiment results on four benchmark datasets demonstrate that our model’s classification accuracy greatly outpaces that of the conventional deep learning text classification model. Our model performs exceptionally well when compared to other text categorization GCN algorithms.

Enhancing accident cause analysis through text classification and accident causation theory: A case study of coal mine gas explosion accidents

Accident analysis is a crucial aspect of accident prevention, and natural language processing (NLP) techniques can efficiently be applied to analyze the causes of accidents. However, existing analysis methods primarily rely on text clustering and lack the application of accident causation theories, leading to a lack of specific accident cause analysis results. Combining text classification techniques with accident causation theories is an effective approach to address this issue. In this study, we integrated text classification techniques with accident causation theories and utilized coal mine gas explosion accidents as an example. We constructed a corpus, trained a BERT model, and evaluated its performance to obtain a text classification model for accident cause analysis. The results indicated that the BERT model-based text classification algorithm had an accuracy and macro-average F1 value of 0.9878 and 0.7792, respectively, significantly outperforming the control model. The application of this approach demonstrated that combining accident causation theories with text classification techniques for accident cause analysis can improve the efficiency of accident analysis while ensuring the richness of details in analyzing accident causes. By efficiently analyzing a large number of accident cases, this approach can provide a data foundation for data-driven accident prevention and technical support for integrated accident prevention.

Convergence of polarized self-attention with consistent rank Chinese text classification

Utilizing the powerful feature extraction capabilities of deep learning, a text classification algorithm with multi-dimensional and high-domain adaptability is designed in this study. This method enhances the models understanding of topics and content by incorporating the Polarized Self-Attention (PSA) module, which strengthens the spatial structure and semantic features of textual information. The loss function is redesigned to assign smaller losses to misclassifications of neighboring categories, allowing the model to optimize classification accuracy while learning hierarchical structural information between categories. Finally, experimental verification is conducted on a publicly available news dataset, demonstrating improved results in text classification achieved by the proposed algorithm.

Improving Performance of Massive Text Real-Time Classification for Document Confidentiality Management

For classified and sensitive electronic documents within the scope of enterprises and organizations, in order to standardize and strengthen the confidentiality management of enterprises and meet the actual needs of secret text classification, a document automatic classification optimization method based on keyword retrieval and the kNN classification algorithm is proposed. The method supports keyword classification management, provides users with keywords of multiple risk levels, and then combines a matching scanning algorithm to label keywords of different levels. The text with labels is used as the training set of the kNN algorithm to classify the target text and realize the classification protection of text data. Aimed at solving the shortcomings of large feature vector dimension, low classification efficiency, and low accuracy in existing kNN text classification methods, an optimization method is proposed using a feature selection algorithm and a kNN algorithm based on an AVX instruction set to realize real-time classification of massive texts. By constructing a keyword dictionary and an optimized feature vector, parallel calculation of the feature vector weight and distance vector is realized, and the accuracy and efficiency of text classification are improved. The experimental results show that the multi-classification effect of the feature selection algorithm used in this paper, tf-DE, is better than that of the traditional tf-idf algorithm, and the classification effect of kNN is comparable to that of the support vector machine (SVM) algorithm. With the increase in feature vector dimensions, the classification effect of the text classification algorithm is improved and the classification time also increases linearly. The AVX-256 acceleration method takes about 55% of the time of the original version, thus verifying the effect of multi-classification of massive texts for document confidentiality management.

A method for optimizing text preprocessing and text classification using multiple cycles of learning with an application on shipbrokers emails

Optimizing text preprocessing and text classification algorithms is an important, everyday task in large organizations and companies and it usually involves a labor-intensive and time-consuming effort. For example, the filtering and sorting of a large number of electronic mails (emails) are crucial to keeping track of the received information and converting it automatically into useful and profitable knowledge. Business emails are often unstructured, noisy, and with many abbreviations and acronyms, which makes their handling a challenging procedure. To overcome those challenges, a two-step classification approach is proposed, along with a two-cycle labeling procedure in order to speed up the labeling process. Every step incorporates a heuristic classification approach to assign emails to predefined classes by comparing several classification and text vectorization algorithms. These algorithms are compared and evaluated using the F1 score and balanced accuracy. The implementation of the proposed algorithm is demonstrated in a shipbroker agent operating in Greece with excellent performance, improving organization and administration while reducing expenses.

Enhanced text classification through an improved discrete laying chicken algorithm

AbstractThe exponential growth of digital text documents presents a significant challenge for text classification algorithms, as the vast number of words in each document can hinder their efficiency. Feature selection (FS) is a crucial technique that aims to eliminate irrelevant features and enhance classification accuracy. In this study, we propose an improved version of the discrete laying chicken algorithm (IDLCA) that utilizes noun‐based filtering to reduce the number of features and improve text classification performance. Although LCA is a newly proposed algorithm, it has not been systematically applied to discrete problems before. Our enhanced version of LCA employs different operators to improve both exploration and exploitation of this algorithm to find better solutions in discrete mode. To evaluate the effectiveness of the proposed method, we compared it with some conventional nature‐inspired feature selection methods using various learning models such as decision trees (DT), K‐nearest neighbor (KNN), Naive Bayes (NB), and support vector machine (SVM) on five benchmark datasets with three different evaluation metrics. The experimental results demonstrate the effectiveness of the proposed algorithm in comparison to the existing one. The code is available at https://github.com/m0javad/Improved-Discrete-Laying-Chicken-Algorithm.

Automation of text document classification in the budgeting phase of the Construction process: a Systematic Literature Review

PurposeThis study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.Design/methodology/approachThis paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.FindingsThis work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.Originality/valueGiven the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

A multi-feature fusion model based on long and short term memory network and improved artificial bee colony algorithm for Esnglish text classification

The traditional methods of English text classification have two disadvantages. One is that they cannot fully represent the semantic information of the text. The other is that they cannot fully extract and integrate the global and local information of the text. Therefore, we propose a multi-feature fusion model based on long and short term memory network and improved artificial bee colony algorithm for English text classification. In this method, the character-level vector and word-level vector representations of English text are calculated using a pre-training model to obtain a more comprehensive text feature vector representation. Then the multi-head attention mechanism is used to capture the dependencies in the text sequence to improve the semantic understanding of the text. Through feature fusion, the channel features are optimized and the spatial features and time series features are combined to improve the classification performance of the hybrid model. In the stage of network training, the weighted linear combination of maximum Shannon entropy and minimum cross entropy is used as the return degree evaluation function of the bee colony algorithm, and the scale factor is introduced to adjust the solution search strategy of leading bees and following bees, and the improved artificial bee colony algorithm is combined with the classification network to realize the automatic optimization and adjustment of network parameters. Experiments are carried out on public data set. Compared with traditional convolutional neural networks, the classification accuracy of the new model increases by 2% on average, and the accuracy of data set increases by 2.4% at the highest.

Improvement of Translation Accuracy for the Word Sense Disambiguation System using Novel Classifier Approach

Machine Translation (MT) is a crucial application of Natural language Processing (NLP). This MT technique automatic and based on computers. One of the most modern techniques adopted in MT is Machine Learning (ML). Over the past few years, ML has grown in popularity during MT process among researchers. Ambiguity is a major challenge in MT. Word Sense Disambiguation (WSD) is a common technique for solving the ambiguity problem. ML approaches are commonly used for the WSD techniques and are used for training and testing purposes. The outcome prediction of the test data gives encouraging results. Text classification is one of the most significant techniques for resolving the WSD. In this paper, we have analyzed some common supervised ML text classification algorithms and also proposed a “hybrid model” called “AmbiF.” We have compared the results of all analyzed algorithms with the proposed model “AmbiF. The analyzed supervised algorithms are Decision Tree (DT), Bayesian network, Support Vector Machines (SVMs), K-Nearest Neighbor (KNN), Random Forest (RF), and Logistic Regression (LR). The range of accuracy for all the algorithms that were examined is between sixty-eight and eighty-four percent. To improve the accuracy of the AmbiF model, we have merged the DT, SVM, and Naïve Bayes (NB)-classifier approach. For testing the model, we have used the ten-fold cross-validation test method. The AmbiF model’s accuracy has been reported eighty-five percent. Comparing the AmbiF model to all other analyzed supervised ML classification algorithms, it has also demonstrated great precision, recall, and F-score. Waikato Environment for Knowledge Analysis (WEKA)’s ML-tool is used to analyze the algorithms and the AmbiF model.

ALGORITMO DE APRENDIZAGEM PROFUNDA COMPREENSIVA PARA COMPREENDER O PAPEL DAS REDES SOCIAIS NA PERCEPÇÃO DO CONSUMIDOR EM RELAÇÃO AO CONSUMO SUSTENTÁVEL

ABSTRACT This research proposes a comprehensive deep-learning algorithm to understand the role of social media in consumer perception of green consumption. After the COVID-19 pandemic, society has shown increased focus on the relationship between people and nature. Achieving sustainable development goals requires promoting green consumption, which necessitates understanding and influencing public attitudes toward sustainability. While previous studies have explored green consumption using behavioral models and surveys, they often overlook the perspective of social media. This study uses deep learning techniques to analyze social media data, including text and video content, to gain insights into consumer behavior and preferences. The study entails collecting data from X (former Twitter) and YouTube, developing deep learning algorithms for text classification, and creating a visualization and reporting system. More specifically, this study aims to analyze the impact of social media information sharing on society’s green purchasing intentions and proposes advanced architectures for text mining specifically the LDA method. This study highlights the valuable insights from analyzing social media discourse on green consumption. Trends, emotional attitudes, and engagement were examined using text mining and sentiment analysis. The study reveals platform-specific differences in sentiment and identifies influential keywords and phrases. The analysis also uncovers emotional responses and key factors associated with the discourse on green consumption. The findings can inform future strategies for promoting sustainable consumption. The study concludes by emphasizing the importance of further research to explore the discrepancies between platforms and harness the implications of these findings for sustainable consumption strategies.

Computational analysis of dystopian elements in the partition fiction: A machine learning approach to the indian English novels

Despite the growth of digital humanities, the specific application of computational methods to analyse literary themes and elements remains underexplored. This study aims to use machine learning algorithms for text classification on seven novels about the partition of India. The article has a dual objective: firstly, to develop and implement an innovative opinion mining framework that leverages machine learning techniques to identify and classify dystopian elements in Partition novels; and secondly, to evaluate and compare the efficacy of the model in accurately classifying dystopian sentiments within selected literary texts. The proposed framework includes six phases of data collection, test-pre-processing, text extraction, text exploration, modelling or classification using machine leaning, and performance evaluation. Machine learning approaches such as Logistic Regression, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor are adopted to classify the text based on compiled dictionary. First, the study revealed that the main components and characteristics of dystopian elements found in selected narratives are ‘fear’, ‘suffer’, ‘oppression’ and ‘violence’. Second, the analysis showed that SVM with CountVectorizer, using imbalanced dataset and random oversampling dataset, outperforms other classifiers in classifying dystopian types in the selected novels. The results also suggest that CountVectorizer works better for the dataset compared to TF-IDF.

The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things

The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things