Short-Text Sentiment Analysis Based on Windowed Word Vector

被引：0

作者：

Zhao, Dongmei ^{[1
]}

Shen, Yingli ^{[1
]}

Shen, Yabo ^{[1
]}

Ma, Yong ^{[1
]}

Jin, Yun ^{[1
,2
]}

Li, Shidang ^{[1
]}

Gu, Mingliang ^{[1
]}

机构：

[1] Jiangsu Normal Univ, Sch Phys & Elect Engn, 101 Shanghai Rd, Xuzhou 221116, Jiangsu, Peoples R China

[2] Southeast Univ, Key Lab Children Dev & Learning Sci, Minist Educ, Nanjing, Peoples R China

来源：

COMMUNICATIONS, SIGNAL PROCESSING, AND SYSTEMS, CSPS 2018, VOL III: SYSTEMS | 2020年 / 517卷

基金：

中国博士后科学基金;

关键词：

Sentiment analysis; Windowed word vector; Classifier fusion; Short text;

D O I：

10.1007/978-981-13-6508-9_22

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The traditional text sentiment analysis directly inputs syntactic feature or word vector to model. It fails to consider the characteristics of time series in sentiment. Considering that product reviews are short text, this paper comes up with the method of windowed word vector and classifier fusion in the decision layer. The results indicate that the proposed method can achieve better performance than several existing methods.

引用

页码：168 / 174

页数：7

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