Semantic Genetic Programming for Sentiment Analysis

被引：13

作者：

Graff, Mario ^{[1
]}

Tellez, Eric S. ^{[1
]}

Jair Escalante, Hugo ^{[2
]}

Miranda-Jimenez, Sabino ^{[1
]}

机构：

[1] CONACYT INFOTEC Ctr Invest & Innovac Tecnol Infor, Aguascalientes, Mexico

[2] Inst Nacl Astrofis Opt & Electr, Comp Sci Dept, Cholula, Mexico

来源：

NEO 2015 | 2017年 / 663卷

关键词：

Semantic crossover; Sentiment analysis; Genetic programming; Text mining; LARGE-SCALE; SYMBOLIC REGRESSION;

D O I：

10.1007/978-3-319-44003-3_2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Sentiment analysis is one of the most important tasks in text mining. This field has a high impact for government and private companies to support major decision-making policies. Even though Genetic Programming (GP) has been widely used to solve real world problems, GP is seldom used to tackle this trendy problem. This contribution starts rectifying this research gap by proposing a novel GP system, namely, Root Genetic Programming, and extending our previous genetic operators based on projections on the phenotype space. The results show that these systems are able to tackle this problem being competitive with other state-of-the-art classifiers, and, also, give insight to approach large scale problems represented on high dimensional spaces.

引用

页码：43 / 65

页数：23

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