Knowledge-Based Approaches to Concept-Level Sentiment Analysis INTRODUCTION

被引：81

作者：

Cambria, Erik ^{[1
]}

Schuller, Bjoern ^{[2
]}

Liu, Bing ^{[3
]}

Wang, Haixun

Havasi, Catherine ^{[4
]}

机构：

[1] Natl Univ Singapore, Temasek Labs, Cognit Sci Programme, Singapore 117548, Singapore

[2] Tech Univ Munich, Inst Human Machine Commun, Machine Intelligence & Signal Proc Grp, D-80290 Munich, Germany

[3] Univ Illinois, Chicago, IL USA

[4] MIT, Open Mind Common Sense Project, Media Lab, Cambridge, MA USA

来源：

IEEE INTELLIGENT SYSTEMS | 2013年 / 28卷 / 02期

关键词：

concept-level sentiment analysis; data mining; intelligent systems; knowledge mining; online social data; opinion mining;

D O I：

10.1109/MIS.2013.45

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The guest editors introduce novel approaches to opinion mining and sentiment analysis that go beyond a mere word-level analysis of text and provide concept-level methods. Such approaches allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain. © 2001-2011 IEEE.

引用

页码：12 / 14

页数：3