Aspect-Level Sentiment Analysis of Online Product Reviews Based on Multi-features

被引:0
|
作者
Wang, Binhui [1 ]
Wang, Ruiqi [1 ]
Liu, Shujun [1 ]
Chai, Yanyu [1 ]
Xing, Shusong [1 ]
机构
[1] Nankai Univ, Coll Software, Tianjin, Peoples R China
来源
SEMANTIC TECHNOLOGY, JIST 2019 | 2020年 / 1157卷
关键词
Aspect-level sentiment analysis; Feature fusion; Implicit aspect;
D O I
10.1007/978-981-15-3412-6_16
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Aspect-level sentiment analysis aims to identify the sentiment polarity of fine-grained opinion targets. Existing methods are usually performed on structured standard datasets. We propose a model for a specific dataset which has a complex structure. First, we utilize some matching rules to extract implicit aspects, then we use the extracted aspect words to segment the corpus into samples. Finally, we propose a set of methods to construct data-based features, and try to fuse multi-features for classifier training. Experiments show that the method integrated three features has the highest F1 score, and the sentiment analysis results are more accurate.
引用
收藏
页码:161 / 169
页数:9
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