Document Classification Process Research Articles

Automating Lexicon Generation: A Comprehensive Review of Alternative Approaches

Lexicon-based approaches to Document Classification are widely used, but the manual construction of lexicons can be time-consuming and resource-intensive. In this paper, we propose methods for automating the generation of lexicons later used for Document Classification. We explored diverse methods for generating lexicons, including semantic matches, frequency-based approaches, machine learning algorithms, and large language model techniques. We, later, used these lexicons to classify documents based on their content. By comparing our different lexicons results on a same task, based on criteria such as scalability and the F1 score, we determine optimized use-case for those methods. We show that our automated approaches are effective and efficient, producing accurate classifications with minimal human intervention. Some approaches have the potential to streamline the document classification process, reducing the time and resources required for manual lexicon generation, it also gives optimized use-case for the different methods. Thereafter, we discussed the obtained results.

Design of travel route recommendation system based on fast Spark artificial intelligence architecture

In order to overcome the problems of low accuracy of current system tourism route prediction and poor accuracy of route recommendation, this paper proposes a tourism route recommendation system design based on fast Spark artificial intelligence architecture. In the hardware part, the search process structure of tourist route, web page information classification and extraction module and tourist document classification process structure are designed. For the software part, firstly the problem is defined for the tourist route recommendation, secondly the Markov model is constructed, and finally the tourist route is mined. By ranking the transfer probability of tourist attractions, the tourist route can be obtained, and finally the tourist route recommendation is realised. The experimental results show that the designed system has a prediction accuracy of more than 70% and a recommendation accuracy of more than 80%, with high prediction accuracy and route recommendation accuracy.

Text document classification using fuzzy rough set based on robust nearest neighbor (FRS-RNN)

The fuzzy rough set (FRS) acts as a powerful mathematical tool to deal with uncertain data, and it has many applications in feature selection, dimensionality reduction and classification. The fuzzy rough set based on robust nearest neighbor (FRS-RNN) is one of the vital classifiers which has been successfully applied to handle real-valued datasets. From the literature, it is very clearly evident that no research attempt has been made on FRS-RNN to text document classification. Generally, the document classification process consists of two crucial phases, namely feature extraction and classifier model construction. Mainly TF-IDF and convolutional neural network (CNN)-based techniques are used for efficient feature extraction. The CNN provides the best feature engineering through effective preprocessing the documents for better representation using pre-trained word embedding. In this paper, we proposed a modified CNN structure for both text document classification and feature extraction. Then, both FRS and FRS-RNN have been implemented for text document classification on the benchmark datasets like 20 Newsgroup and Reuter-21578 using both TF-IDF and modified CNN-based feature extraction techniques. The classification performance of the FRS, CNN and FRS-RNN is evaluated and compared using well-defined metrics like accuracy, precision, recall and F1-measure. Finally, the classification performance of FRS-RNN is compared with state-of-the-art traditional classification models such as SVM, KNN, Naive Bayes, DNN, CNN and RNN and with some recently developed classification models. The experimental results followed by empirical evaluation show that the proposed FRS-RNN outperforms all the aforementioned classification models.

Effect of N-Gram on Document Classification on the Naïve Bayes Classifier Algorithm

News has become a major need for everyone, with news we can get the information needed. News can be distributed in the form of print mass media, electronic mass media and online media. The means of spreading the news now have grown very rapidly, making the amount of information being managed are bigger and word management classified also not small. herefore, we need a system for classifying documents that are not structured. In this study, word processing in a document is done by N-Gram as a feature generation. The document classification process is carried out using the Naïve Bayes Classifier algorithm. This study examines the effect of N-Gram on document classification on the Naïve Bayes Classifier algorithm. The results of the classification accuracy of documents by applying N-Gram is 32.68% and without applying N-Gram is 84.97%. A decrease in the classification results occurs the number of features that result from solving N-Gram that is unique or dominant to another category. The accuracy of the results obtained shows that the application of N-Gram in the classification of documents using the Naïve Bayes Classifier algorithm gives a decreased effect on the performance of the classification

Design of Travel Route Recommendation System Based on Fast Spark Artificial Intelligence Architecture

In order to overcome the problems of low accuracy of current system tourism route prediction and poor accuracy of route recommendation, this paper proposes a tourism route recommendation system design based on fast Spark artificial intelligence architecture. In the hardware part, the search process structure of tourist route, web page information classification and extraction module and tourist document classification process structure are designed. For the software part, firstly the problem is defined for the tourist route recommendation, secondly the Markov model is constructed, and finally the tourist route is mined. By ranking the transfer probability of tourist attractions, the tourist route can be obtained, and finally the tourist route recommendation is realised. The experimental results show that the designed system has a prediction accuracy of more than 70% and a recommendation accuracy of more than 80%, with high prediction accuracy and route recommendation accuracy.

A Hybrid Document Features Extraction with Clustering based Classification Framework on Large Document Sets

As the size of the document collections are increasing day-by-day, finding an essential document clusters for classification problem is one of the major problem due to high inter and intra document variations. Also, most of the conventional classification models such as SVM, neural network and Bayesian models have high true negative rate and error rate for document classification process. In order to improve the computational efficacy of the traditional document classification models, a hybrid feature extraction-based document cluster approach and classification approaches are developed on the large document sets. In the proposed work, a hybrid glove feature selection model is proposed to improve the contextual similarity of the keywords in the large document corpus. In this work, a hybrid document clustering similarity index is optimized to find the essential key document clusters based on the contextual keywords. Finally, a hybrid document classification model is used to classify the clustered documents on large corpus. Experimental results are conducted on different datasets, it is noted that the proposed document clustering-based classification model has high true positive rate, accuracy and low error rate than the conventional models.

A Novel Approach for Ontology-Based Feature Vector Generation for Web Text Document Classification

The task of extracting the used feature vector in mining tasks (classification, clustering …etc.) is considered the most important task for enhancing the text processing capabilities. This paper proposes a novel approach to be used in building the feature vector used in web text document classification process; adding semantics in the generated feature vector. This approach is based on utilizing the benefit of the hierarchal structure of the WordNet ontology, to eliminate meaningless words from the generated feature vector that has no semantic relation with any of WordNet lexical categories; this leads to the reduction of the feature vector size without losing information on the text, also enriching the feature vector by concatenating each word with its corresponding WordNet lexical category. For mining tasks, the Vector Space Model (VSM) is used to represent text documents and the Term Frequency Inverse Document Frequency (TFIDF) is used as a term weighting technique. The proposed ontology based approach was evaluated against the Principal component analysis (PCA) approach, and against an ontology based reduction technique without the process of adding semantics to the generated feature vector using several experiments with five different classifiers (SVM, JRIP, J48, Naive-Bayes, and kNN). The experimental results reveal the effectiveness of the authors' proposed approach against other traditional approaches to achieve a better classification accuracy F-measure, precision, and recall.

A Novel Approach for Ontology-Based Dimensionality Reduction for Web Text Document Classification

Dimensionality reduction of feature vector size plays a vital role in enhancing the text processing capabilities; it aims in reducing the size of the feature vector used in the mining tasks (classification, clustering, etc.). This paper proposes an efficient approach to be used in reducing the size of the feature vector for web text document classification process. This approach is based on using WordNet ontology, utilizing the benefit of its hierarchal structure, to eliminate words from the generated feature vector that has no relation with any of WordNet lexical categories; this leads to the reduction of the feature vector size without losing information on the text. For mining tasks, the Vector Space Model (VSM) is used to represent text documents and the Term Frequency Inverse Document Frequency (TFIDF) is used as a term weighting method. The proposed ontology based approach was evaluated against the Principal component analysis (PCA) approach using several experiments. The experimental results reveal the effectiveness of the authors' proposed approach against other traditional approaches to achieve a better classification accuracy F-measure, precision, and recall.

EMPIRICAL EVALUATION OF MACHINE LEARNING ALGORITHMS FOR AUTOMATIC DOCUMENT CLASSIFICATION

Automatic document classification process is the important area of research in the field of Text Mining(TM). Text mining is the process of discovering the interesting pattern or knowledge from huge amount of data. The document classification process used in many domains. Here, to take the classification process is apply SMS spam classification. The bench marked dataset is used and the same data set is processed in various ML algorithms of Naive Bayes, Support Vector Machine, Decision Tree and Logistic Regression model. In this paper evaluates the results of various machine learning algorithms for automatic document classification in SMS spam classification.