A Rule-Based Holistic Approach for Turkish Aspect-Based Sentiment Analysis

被引：0

作者：

Bayraktar, Kivanc ^{[1
]}

Yavanoglu, Uraz ^{[1
]}

Ozbilen, Alper ^{[2
]}

机构：

[1] Gazi Univ, Comp Engn, Ankara, Turkey

[2] Gazi Univ, Informat Secur Engn, Ankara, Turkey

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2019年

关键词：

sentiment analysis; aspect-based sentiment analysis; Turkish sentiment analysis;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this study, a holistic method which uses statistical, linguistic and rule-based approaches for Turkish aspect-based sentiment analysis is proposed. The proposed method has been tested on the Turkish restaurant dataset created within the scope of SemEval Aspect Based Sentiment Analysis (ABSA) 2016. Firstly, candidate aspect terms were acquired employing LDA, C-value and WSBFE. Afterwards, aspect terms were found by rule-based approach and aspect-sentiment pairs were determined. In aspect term extraction 56,28% f-score was obtained while in aspect-sentiment matching phase 52,05% accuracy was achieved.

引用

页码：2154 / 2158

页数：5

共 27 条

[1] Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews [J].