SVM and k-Means Hybrid Method for Textual Data Sentiment Analysis

被引：13

作者：

Korovkinas, Konstantinas ^{[1
]}

Danenas, Paulius ^{[2
]}

Garsva, Gintautas ^{[1
]}

机构：

[1] Vilnius Univ, Inst Social Sci & Appl Informat, Muitines Str 8, Kaunas, Lithuania

[2] Kaunas Univ Technol, Ctr Informat Syst Design Technol, Dept Informat Syst, Studentu Str 50-313a, Kaunas, Lithuania

来源：

BALTIC JOURNAL OF MODERN COMPUTING | 2019年 / 7卷 / 01期

关键词：

SVM; k-Means; sentiment analysis; SUPPORT VECTOR MACHINE; OPTIMIZATION; CLASSIFICATION; PARAMETERS;

D O I：

10.22364/bjmc.2019.7.1.04

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The goal of this paper is to propose a hybrid technique to improve Support Vector Machines classification accuracy using training data sampling and hyperparameter tuning. The proposed technique applies clustering to select training data and parameter tuning to optimize classifier effectiveness. The paper reports that better results were obtained using our proposed method in all experiments, compared to results of method presented in our previous work.

引用

页码：47 / 60

页数：14

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