Bibliometric Survey on Incremental Learning in Text Classification Algorithms for False Information Detection

The false information or misinformation over the web has severe effects on people, business and society as a whole. Therefore, detection of misinformation has become a topic of research among many researchers. Detecting misinformation of textual articles is directly connected to text classification problem. With the massive and dynamic generation of unstructured textual documents over the web, incremental learning in text classification has gained more popularity. This survey explores recent advancements in incremental learning in text classification and review the research publications of the area from Scopus, Web of Science, Google Scholar, and IEEE databases and perform quantitative analysis by using methods such as publication statistics, collaboration degree, research network analysis, and citation analysis. The contribution of this study in incremental learning in text classification provides researchers insights on the latest status of the research through literature survey, and helps the researchers to know the various applications and the techniques used recently in the field

Enhanced ontology-based text classification algorithm for structurally organized documents

Text classification (TC) is an important foundation of information retrieval and text mining. The main task of a TC is to predict the text‟s class according to the type of tag given in advance. Most TC algorithms used terms in representing the document which does not consider the relations among the terms. These algorithms represent documents in a space where every word is assumed to be a dimension. As a result such representations generate high dimensionality which gives a negative effect on the classification performance. The objectives of this thesis are to formulate algorithms for classifying text by creating suitable feature vector and reducing the dimension of data which will enhance the classification accuracy. This research combines the ontology and text representation for classification by developing five algorithms. The first and second algorithms namely Concept Feature Vector (CFV) and Structure Feature Vector (SFV), create feature vector to represent the document. The third algorithm is the Ontology Based Text Classification (OBTC) and is designed to reduce the dimensionality of training sets. The fourth and fifth algorithms, Concept Feature Vector_Text Classification (CFV_TC) and Structure Feature Vector_Text Classification (SFV_TC) classify the document to its related set of classes. These proposed algorithms were tested on five different scientific paper datasets downloaded from different digital libraries and repositories. Experimental obtained from the proposed algorithm, CFV_TC and SFV_TC shown better average results in terms of precision, recall, f-measure and accuracy compared against SVM and RSS approaches. The work in this study contributes to exploring the related document in information retrieval and text mining research by using ontology in TC

Hybrid Approach Combining Machine Learning and a Rule-Based Expert System for Text Categorization

This paper discusses a novel hybrid approach for text categorization that combines a machine learning algorithm, which provides a base model trained with a labeled corpus, with a rule-based expert system, which is used to improve the results provided by the previous classifier, by filtering false positives and dealing with false negatives. The main advantage is that the system can be easily fine-tuned by adding specific rules for those noisy or conflicting categories that have not been successfully trained. We also describe an implementation based on k-Nearest Neighbor and a simple rule language to express lists of positive, negative and relevant (multiword) terms appearing in the input text. The system is evaluated in several scenarios, including the popular Reuters-21578 news corpus for comparison to other approaches, and categorization using IPTC metadata, EUROVOC thesaurus and others. Results show that this approach achieves a precision that is comparable to top ranked methods, with the added value that it does not require a demanding human expert workload to trai

State of the art document clustering algorithms based on semantic similarity

The constant success of the Internet made the number of text documents in electronic forms increases hugely. The techniques to group these documents into meaningful clusters are becoming critical missions. The traditional clustering method was based on statistical features, and the clustering was done using a syntactic notion rather than semantically. However, these techniques resulted in un-similar data gathered in the same group due to polysemy and synonymy problems. The important solution to this issue is to document clustering based on semantic similarity, in which the documents are grouped according to the meaning and not keywords. In this research, eighty papers that use semantic similarity in different fields have been reviewed; forty of them that are using semantic similarity based on document clustering in seven recent years have been selected for a deep study, published between the years 2014 to 2020. A comprehensive literature review for all the selected papers is stated. Detailed research and comparison regarding their clustering algorithms, utilized tools, and methods of evaluation are given. This helps in the implementation and evaluation of the clustering of documents. The exposed research is used in the same direction when preparing the proposed research. Finally, an intensive discussion comparing the works is presented, and the result of our research is shown in figures

Information retrieval approaches: A comparative study

The area of information retrieval (IR) has taken on increasing importance in recent years. This field is now of interest to large communities in several application domains (security, medicine, aeronautics, etc.). IR studies find relevant information from the semi-unstructured type of data. As the information resources generated after the search can be extensive in quantity and different in quality, it is essential to rank these results according to the degree of relevance. This paper focuses on text information retrieval (TIR) and emphasizes the importance of each IR approach. This study presents insightful aspects of TIR and provides a comparative study between some proposed approaches and models. Each model offers IR advantages and suffers from several limitations

Techniques for text classification: Literature review and current trends

Automated classification of text into predefined categories has always been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. This kind of web information, popularly known as the digital/electronic information is in the form of documents, conference material, publications, journals, editorials, web pages, e-mail etc. People largely access information from these online sources rather than being limited to archaic paper sources like books, magazines, newspapers etc. But the main problem is that this enormous information lacks organization which makes it difficult to manage. Text classification is recognized as one of the key techniques used for organizing such kind of digital data. In this paper we have studied the existing work in the area of text classification which will allow us to have a fair evaluation of the progress made in this field till date. We have investigated the papers to the best of our knowledge and have tried to summarize all existing information in a comprehensive and succinct manner. The studies have been summarized in a tabular form according to the publication year considering numerous key perspectives. The main emphasis is laid on various steps involved in text classification process viz. document representation methods, feature selection methods, data mining methods and the evaluation technique used by each study to carry out the results on a particular dataset

Hybrid feature selection based on principal component analysis and grey wolf optimizer algorithm for Arabic news article classification

The rapid growth of electronic documents has resulted from the expansion and development of internet technologies. Text-documents classification is a key task in natural language processing that converts unstructured data into structured form and then extract knowledge from it. This conversion generates a high dimensional data that needs further analusis using data mining techniques like feature extraction, feature selection, and classification to derive meaningful insights from the data. Feature selection is a technique used for reducing dimensionality in order to prune the feature space and, as a result, lowering the computational cost and enhancing classification accuracy. This work presents a hybrid filter-wrapper method based on Principal Component Analysis (PCA) as a filter approach to select an appropriate and informative subset of features and Grey Wolf Optimizer (GWO) as wrapper approach (PCA-GWO) to select further informative features. Logistic Regression (LR) is used as an elevator to test the classification accuracy of candidate feature subsets produced by GWO. Three Arabic datasets, namely Alkhaleej, Akhbarona, and Arabiya, are used to assess the efficiency of the proposed method. The experimental results confirm that the proposed method based on PCA-GWO outperforms the baseline classifiers with/without feature selection and other feature selection approaches in terms of classification accuracy

Network problems detection and classification by analyzing syslog data

Network troubleshooting is an important process which has a wide research field. The first step in troubleshooting procedures is to collect information in order to diagnose the problems. Syslog messages which are sent by almost all network devices contain a massive amount of data related to the network problems. It is found that in many studies conducted previously, analyzing syslog data which can be a guideline for network problems and their causes was used. Detecting network problems could be more efficient if the detected problems have been classified in terms of network layers. Classifying syslog data needs to identify the syslog messages that describe the network problems for each layer, taking into account the different formats of various syslog for vendors’ devices. This study provides a method to classify syslog messages that indicates the network problem in terms of network layers. The method used data mining tool to classify the syslog messages while the description part of the syslog message was used for classification process. Related syslog messages were identified; features were then selected to train the classifiers. Six classification algorithms were learned; LibSVM, SMO, KNN, Naïve Bayes, J48, and Random Forest. A real data set which was obtained from the Universiti Utara Malaysia’s (UUM) network devices is used for the prediction stage. Results indicate that SVM shows the best performance during the training and prediction stages. This study contributes to the field of network troubleshooting, and the field of text data classification