Text classification toward a scientific forum

Applied Soft Computing, 2007

This paper presents a least square support vector machine (LS-SVM) that performs text classification of noisy document titles according to different predetermined categories. The system's potential is demonstrated with a corpus of 91,229 words from University of Denver's Penrose Library catalogue. The classification accuracy of the proposed LS-SVM based system is found to be over 99.9%. The final classifier is an LS-SVM array with Gaussian radial basis function (GRBF) kernel, which uses the coefficients generated by the latent semantic indexing algorithm for classification of the text titles. These coefficients are also used to generate the confidence factors for the inference engine that present the final decision of the entire classifier. The system is also compared with a K-nearest neighbor (KNN) and Naïve Bayes (NB) classifier and the comparison clearly claims that the proposed LS-SVM based architecture outperforms the KNN and NB based system. The comparison between the conventional linear SVM based classifiers and neural network based classifying agents shows that the LS-SVM with LSI based classifying agents improves text categorization performance significantly and holds a lot of potential for developing robust learning based agents for text classification. # 2006 Published by Elsevier B.V.

Text classification is the process of assigning text documents based on certain categories. A classifier is used to define the appropriate class for each text document based on the input algorithm used for classification. Due to the emerging trends in the field of internet and computers ,billions of text data are processed at a given time and so there is a need for organizing these data to provide easy storage and accessing .Many text classification approaches were developed for effectively solving the problem of identifying and classifying these data .In this project a new text document classifier is proposed by integrating the nearest neighbor classification approach with the support vector machine(SVM) training algorithm. The proposed SVM-NN approach aims to reduce the impact of parameters in classification accuracy. In the training stage, the SVM is utilized to reduce the training samples for each of the available categories to their support vectors (SVs).The SVs from different categories are used as the training data of nearest neighbor classification algorithm in which the nearest centroid distance function is used to calculate the average distance instead of Euclidean function, which reduce time consumption.

International Journal of Advance Research, Ideas and Innovations in Technology, 2021

Cyberspace has elevated business insights and created a virtual space to store all forms of information online. Due to the rapid development in the online world, the usage of digital documents has increased because it is comfortable for the users to share, update or keep track of the records in one place without losing data. However, maintaining massive data does not suit optimal decision-making and is extremely expensive for storage, processing, and collection. There is a gigantic possibility that human annotators make errors while classifying data because of distraction, monotony, fatigue, and failure to meet the requirements. Once the text classification method uses machine learning approaches, the process will execute with fewer mistakes and more accuracy. The main goal of this review paper is to highlight and explain the role of different machine learning methodologies in text classification. Concurrently, this paper describes the challenges faced by other machine learning techniques and text representation. Furthermore, this review paper will provide an extensive survey on how various machine learning techniques such as Neural Networks, Naive Bayes, Logistic Regression, Random Forest, Decision Trees, and Support Vector Machine (SVM)-are implemented in Text classification.

SURJ, 2018

Text Classification is a need of day, large text existing in the form of stories, news etc. Likewise, this system came into being along several techniques like, Support Vector Machine, Neural Networks and Decision Tree. Stories, newspapers are the page collection that belongs to text categorization. Various Sindhi newspapers are regularly published and Daily Kawish is one of them. People are facing difficulties during reading newspaper because there is no any specific option that will categorize particular news related to sports, technologies, crime, fashion and current affairs. For this purpose, a Text Categorization System (TCS) for Sindhi language is presented in this paper. Five classes are used and scanned each newspaper page inside a single class. It is too difficult to predict how many users will read newspaper simultaneously and for this, web performance is tested. Moreover, for the classification of the text from pages, precision, recall and f-measure are used to measure and achieved 67% of accuracy to classify the text from newspaper pages. It would be beneficial for those who want to save their precious time during reading newspaper.

This research focuses on Text Classification. Text classification is the task of automatically sorting a set of documents into categories from a predefined set. The domain of this research is the combination of information retrieval (IR) technology, Data mining and machine learning (ML) technology. This research will outline the fundamental traits of the technologies involved. This research uses three text classification algorithms (Naive Bayes, VSM for text classification and the new technique-Use of Stanford Tagger for text classification) to classify documents into different categories, which is trained on two different datasets (20 Newsgroups and New news dataset for five categories).In regards to the above classification strategies, Naïve Bayes is potentially good at serving as a text classification model due to its simplicity.

–Text classification is used to classify the document of similar types. Text classification can be also performed under supervision i.e. it is an supervised leaning technique Text classification is a process in which documents are sorted spontaneously into different classes using predefined set. The main issue is that large scale of information lacks organization which makes it difficult to manage. Text classification is identified as one of the key methods used for recognizing such types of digital information. Text classification have various applications such as in information retrieval, natural language processing, automatic indexing, text filtering, image processing, etc. Text classification is also used to process the big data and it can also be used to predict the class labels for newly added data. Text classification is also being used in academic and industries to classify the unstructured data. There are various types of the text classification approaches such as decision tree, SVM, Naïve Bayes etc. In this survey paper, we have analysed the various text classification techniques such as decision tree, SVM, Naïve Bayes etc. These techniques have their individual set of advantages which make them suitable in almost all classification jobs. In this paper we have also analysed evaluation parameters such as F-measure, G-measure and accuracy used in various research works. .

— Text mining is drawing enormous attention in this era as there is a huge amount of text data getting generated and it is required very hardly to manage this data to grasp maximum benefit out of it. Text classification is an essential sub-part of text mining where the related text data is assigned to a particular predefined category. In our study, we discussed different classifier techniques which are popularly used in recent years. There is comparison between different classifiers like SVM, Naïve Bayes, Neural Networks etc. which is expressed in a tabular form in this paper.

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

2004

In the application domain of stock portfolio management, software agents that evaluate the risks associated with the individual companies of a portfolio should be able to read electronic news articles that are written to give investors an indication of the financial outlook of a company. There is a positive correlation between news reports on a company's financial outlook and the company's attractiveness as an investment. However, because of the volume of such reports, it is impossible for financial analysts or investors to track and read each one. Therefore, it would be very helpful to have a system that automatically classifies news reports that reflect positively or negatively on a company's financial outlook. To accomplish this task, we treat the analysis of news articles as a text classification problem. We developed a text classification algorithm that classifies financial news article by using a combination of a reduced but highly informative word feature sets and a variant of weighted majority algorithm. By clustering words represented in latent semantic vector space by LSA into groups with similar concepts, we are able to find semantically coherent word groups. A learning method with unlabeled data "Self-Confident" sampling was proposed to handle the problem of expensive data labeling. Vote entropy is the criterion that information-theoretically assigns a label to an unlabeled document. In comparison with naive Bayes classification boosted by Expectation Maximization (EM), the proposed method showed a better performance in terms of accuracy. Two criteria are used to evaluate methods: how well they improve their performances with unlabeled data after being initially trained on a small number of human-labeled articles and how well they classify the latest financial news articles which are mostly not seen during the training. The contribution of this work lies in the new classification method that we propose and in the sampling technique we used for improving classification accuracy.

International journal of engineering research and technology, 2019

In today’s world, the usage of digitalized text documents has drastically increased. The reason behind this is the growing need for portability of text related files and the greater need to eliminate the dependence on paper. Previously, the task of document classification was handled by very experienced experts who are capable of classifying large text documents into their corresponding category. Overtime, it was realized that this task extremely time consuming. Therefore the need for automatic text document classification came into the big picture. Corresponding research has shown the involvement of various classification algorithms to create an automated text document classification system. The major tasks involved in creating this type of automated system is handling large amount of texts, selecting the features from a wide range of availability and eventually selecting the classification algorithm which is best suited for classification text files. Initially the predefined class...