Targeted Aspect-Based Sentiment Analysis for Lithuanian Social Media Reviews

被引：0
|
作者：
Petkevicius, Mazvydas [1 ]
Vitkute-Adzgauskiene, Daiva [1 ]
Amilevicius, Darius [1 ]
机构：
[1] Vytautas Magnus Univ, Kaunas, Lithuania
来源：
HUMAN LANGUAGE TECHNOLOGIES - THE BALTIC PERSPECTIVE (HLT 2020) | 2020年 / 328卷
关键词：
Targeted aspect-based sentiment analysis; entity recognition; deep learning; social media;
D O I：
10.3233/FAIA200599
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper presents research results for solving the task of targeted aspect-based sentiment analysis in the specific domain of Lithuanian social media reviews. Methodology, system architecture, relevant NLP tools and resources are described, finalized by experimental results showing that our solution is suitable for solving targeted aspect-based sentiment analysis tasks for under-resourced, morphologically rich and flexible word order languages.
引用
收藏
页码：32 / 38
页数：7
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