scholarly journals A word classification method for the recognition of Japanese from a large vocabulary

1987 ◽  
Vol 82 (S1) ◽  
pp. S35-S35
Author(s):  
Kazunaga Yoshida
Keyword(s):  
Classification Method ◽  
Large Vocabulary ◽  
Word Classification

Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis

2018 ◽  
Vol 13 (3) ◽  
pp. 99-111 ◽  
Author(s):  
Srinidhi Hiriyannaiah ◽  
G.M. Siddesh ◽  
K.G. Srinivasa
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Streaming Data ◽  
Classification Method ◽  
Emotion Classification ◽  
Knowledge Based ◽  
Twitter Data ◽  
Internet Users ◽  
Word Classification ◽  
Three Stages

This article describes how recent advances in computing have led to an increase in the generation of data in fields such as social media, medical, power and others. With the rapid increase in internet users, social media has given power for sentiment analysis or opinion mining. It is a highly challenging task for storing, querying and analyzing such types of data. This article aims at providing a solution to store, query and analyze streaming data using Apache Kafka as the platform and twitter data as an example for analysis. A three-way classification method is proposed for sentimental analysis of twitter data that combines both the approaches for knowledge-based and machine-learning using three stages namely emotion classification, word classification and sentiment classification. The hybrid three-way classification approach was evaluated using a sample of five query strings on twitter and compared with existing emotion classifier, polarity classifier and Naïve Bayes classifier for sentimental analysis. The accuracy of the results of the proposed approach is superior when compared to existing approaches.

Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis

2020 ◽  
pp. 1377-1390
Author(s):  
Srinidhi Hiriyannaiah ◽  
G.M. Siddesh ◽  
K.G. Srinivasa
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Streaming Data ◽  
Classification Method ◽  
Emotion Classification ◽  
Knowledge Based ◽  
Twitter Data ◽  
Internet Users ◽  
Word Classification ◽  
Three Stages

This article describes how recent advances in computing have led to an increase in the generation of data in fields such as social media, medical, power and others. With the rapid increase in internet users, social media has given power for sentiment analysis or opinion mining. It is a highly challenging task for storing, querying and analyzing such types of data. This article aims at providing a solution to store, query and analyze streaming data using Apache Kafka as the platform and twitter data as an example for analysis. A three-way classification method is proposed for sentimental analysis of twitter data that combines both the approaches for knowledge-based and machine-learning using three stages namely emotion classification, word classification and sentiment classification. The hybrid three-way classification approach was evaluated using a sample of five query strings on twitter and compared with existing emotion classifier, polarity classifier and Naïve Bayes classifier for sentimental analysis. The accuracy of the results of the proposed approach is superior when compared to existing approaches.

An Automatic Classification Method for Involuntary and Two Types of Voluntary Blinks

2016 ◽  
Vol 136 (9) ◽  
pp. 1350-1358 ◽  
Author(s):  
Hironobu Sato ◽  
Kiyohiko Abe ◽  
Shoichi Ohi ◽  
Minoru Ohyama
Keyword(s):  
Automatic Classification ◽  
Classification Method

Bone Density Classification Method for Robotic Drilling System

2013 ◽  
Vol 133 (3) ◽  
pp. 328-334 ◽  
Author(s):  
Koyo Yu ◽  
Yuki Saito ◽  
Yusuke Kasahara ◽  
Hiromasa Kawana ◽  
Shin Usuda ◽  
...  
Keyword(s):  
Bone Density ◽  
Classification Method ◽  
Robotic Drilling ◽  
Drilling System

Sử dụng phương pháp thang điểm có trọng số đánh giá tổng hợp tài nguyên khí hậu cho du lịch, nghỉ dưỡng (Tại một số trung tâm du lịch ở Việt Nam)

2008 ◽  
Vol 30 (4) ◽  
pp. 356-362
Author(s):  
Nguyễn Khanh Vân
Keyword(s):  
Classification Method ◽  
Viet Nam ◽  
Bioclimatic Conditions

Using the weighted classification method to evaluate bioclimatic conditions for tourism and concevalescence (in some tourism centers of Vietnam)

Perbandingan CART dan Random Forest untuk Deteksi Kanker berbasis Klasifikasi Data Microarray

2020 ◽  
Vol 4 (5) ◽  
pp. 805-812
Author(s):  
Riska Chairunisa ◽  
Adiwijaya ◽  
Widi Astuti
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Ovarian Cancer ◽  
Random Forest ◽  
Classification And Regression Tree ◽  
Classification Method ◽  
Discrete Wavelet ◽  
Prostate Tumors ◽  
Cancer Data ◽  
Colon Tumors

Cancer is one of the deadliest diseases in the world with a mortality rate of 57,3% in 2018 in Asia. Therefore, early diagnosis is needed to avoid an increase in mortality caused by cancer. As machine learning develops, cancer gene data can be processed using microarrays for early detection of cancer outbreaks. But the problem that microarray has is the number of attributes that are so numerous that it is necessary to do dimensional reduction. To overcome these problems, this study used dimensions reduction Discrete Wavelet Transform (DWT) with Classification and Regression Tree (CART) and Random Forest (RF) as classification method. The purpose of using these two classification methods is to find out which classification method produces the best performance when combined with the DWT dimension reduction. This research use five microarray data, namely Colon Tumors, Breast Cancer, Lung Cancer, Prostate Tumors and Ovarian Cancer from Kent-Ridge Biomedical Dataset. The best accuracy obtained in this study for breast cancer data were 76,92% with CART-DWT, Colon Tumors 90,1% with RF-DWT, lung cancer 100% with RF-DWT, prostate tumors 95,49% with RF-DWT, and ovarian cancer 100% with RF-DWT. From these results it can be concluded that RF-DWT is better than CART-DWT.  

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