@inproceedings{9ebf114b6ff046dcb52310265da99e8f,
title = "基於 BERT-DAOA 的意見目標情感分析",
abstract = "Social media networks provide rich and diverse information, making opinion analysis and network volume analysis a new method to investigate and understand the market. Sentiment analysis aims to determine the emotional category in a given text. Since there might be several targets being commented on in the text, Aspect-base sentiment analysis (ABSA) has been proposed to explore the sentiment categories of a target in different aspects. In this paper, we explore the idea of ABSA for sentiment analysis of singers on social networks. We utilize BERT as the embedding layer method for characters and words to explore the relationship between a given sentence and a mentioned target. We consider two attention mechanisms to enhance the performance. The experimental results show that adding the attention layer on top of BERT outperforms the basic BERT-CLS model.",
keywords = "Aspect-based Sentiment Analysis, Deep Learning, Sentiment Analysis",
author = "Chen, {Chen Yu} and Chang, {Chia Hui}",
note = "Publisher Copyright: {\textcopyright} ROCLING 2020.All rights reserved.; 32nd Conference on Computational Linguistics and Speech Processing, ROCLING 2020 ; Conference date: 24-09-2020 Through 26-09-2020",
year = "2020",
language = "繁體中文",
series = "ROCLING 2020 - 32nd Conference on Computational Linguistics and Speech Processing",
publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
pages = "398--412",
editor = "Jenq-Haur Wang and Ying-Hui Lai and Lung-Hao Lee and Kuan-Yu Chen and Hung-Yi Lee and Chi-Chun Lee and Syu-Siang Wang and Hen-Hsen Huang and Chuan-Ming Liu",
booktitle = "ROCLING 2020 - 32nd Conference on Computational Linguistics and Speech Processing",
}