Application of the Polyhedral Conic Functions Method in the Text Classification and Comparative Analysis

Sati, Nur Uylas; Ordin, Burak

doi:10.1155/2018/5349284

Cited by 5 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the acquired data, the most effective method is automatically selected to transform it into the optimal class or inefficient type, so as to meet the minimum time interval between the extraction of each attribute in the classified text. This is also one of the two main indicators to achieve high accuracy and accuracy [9][10]. There are a variety of tools for text classification, such as text, pictures and videos according to the content, text in real space according to the different recognition fields can be divided into paper documents, and a computer vision algorithm including the characteristics of a defined conceptual structure unit is designed to extract and classify each type of information with specific purposes in the target corpus, Thus, correct conclusions can be drawn when the input and output results are achieved [11][12].…”

Section: Application Of Text Classificationmentioning

confidence: 99%

Automatic Text Classification Model Based on Machine Learning

2023

View full text Add to dashboard Cite

The artificial neural network is composed of RBF algorithm, multi-layer parallel model and other parts. Its working principle is to classify the input image through layer by layer training, and then develop and determine the attributes of each neuron with similar features. This paper studies them based on Bayesian classifier, a common method in machine learning. First of all, before text recognition, we need to select the sample library and data type, and design the parameters according to the requirements to form an efficient automatic text classification. Then, according to the functional modules required by the specific model, select the appropriate language and compile and generate program code to realize the whole process, and carry out simulation tests on the functions of the model. The test results show that this test has prepared different quantities of simple words, short sentences, long articles, professional terms and colloquial expressions for classification tests. The classification accuracy of this model is as high as 90%, the error rate is low, and the classification time is fast, This shows that the model meets the needs of users.

show abstract

Section: Application Of Text Classificationmentioning

confidence: 99%

Automatic Text Classification Model Based on Machine Learning

2023

View full text Add to dashboard Cite

show abstract

“…However, many of the existing dictionaries were developed to be used in general topics or they need to be periodically updated with new slang, such as in [ 2 , 6 , 63 , 64 , 65 ]. Thus, some works use the machine learning approach [ 66 , 67 , 68 ], which can be updated automatically.…”

Section: Related Workmentioning

confidence: 99%

Q-Meter: Quality Monitoring System for Telecommunication Services Based on Sentiment Analysis Using Deep Learning

Vieira

Rosa

Rodríguez

et al. 2021

Sensors

View full text Add to dashboard Cite

A quality monitoring system for telecommunication services is relevant for network operators because it can help to improve users’ quality-of-experience (QoE). In this context, this article proposes a quality monitoring system, named Q-Meter, whose main objective is to improve subscriber complaint detection about telecommunication services using online-social-networks (OSNs). The complaint is detected by sentiment analysis performed by a deep learning algorithm, and the subscriber’s geographical location is extracted to evaluate the signal strength. The regions in which users posted a complaint in OSN are analyzed using a freeware application, which uses the radio base station (RBS) information provided by an open database. Experimental results demonstrated that sentiment analysis based on a convolutional neural network (CNN) and a bidirectional long short-term memory (BLSTM)-recurrent neural network (RNN) with the soft-root-sign (SRS) activation function presented a precision of 97% for weak signal topic classification. Additionally, the results showed that 78.3% of the total number of complaints are related to weak coverage, and 92% of these regions were proved that have coverage problems considering a specific cellular operator. Moreover, a Q-Meter is low cost and easy to integrate into current and next-generation cellular networks, and it will be useful in sensing and monitoring tasks.

show abstract

“…For multiclass classification, Algorithm 1 is applied between each class and the rest. Designed multiclass classification algorithms are tested for text classification and good comparative results are obtained against the state-of-theart text classification methods [21]. The mentioned PCF multiclass classification algorithm using both clustering and misclassifications is given as follows [20]:…”

Section: Algorithm 1: Binary Classification Via Pcfsmentioning

confidence: 99%

A novel semisupervised classification method via membership and polyhedral conic functions

Sati¹

2020

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

In real-world problems, finding sufficient labeled data for defining classification rules is very difficult. This paper suggests a new semisupervised multiclass classification method. In the initialization, new membership functions are defined by utilizing the labeled data's medoids and means. Then the unlabeled points are labeled with the class of the highest membership value. In the supervised learning phase, separation via the polyhedral conic functions (PCFs) approach is improved by using defined membership values in the linear programming problem. The suggested algorithm is tested on real-world datasets and compared with the state-of-the-art semisupervised methods. The results obtained indicate that the suggested algorithm is effective in classification and is worth studying.

show abstract

Application of the Polyhedral Conic Functions Method in the Text Classification and Comparative Analysis

Cited by 5 publications

References 37 publications

Automatic Text Classification Model Based on Machine Learning

Automatic Text Classification Model Based on Machine Learning

Q-Meter: Quality Monitoring System for Telecommunication Services Based on Sentiment Analysis Using Deep Learning

A novel semisupervised classification method via membership and polyhedral conic functions

Contact Info

Product

Resources

About