.png){kind=icon}
返回
Exploring Deep Recurrent Convolution Neural Networks for Subjectivity Classification
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2018.2885362.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Subjectivity classification aims to discriminate whether the given text presents opinions or states facts, which is an important foundation and component of sentiment analysis task. The characteristics of microblog (e.g., short text, full noise, casual expression, and incomplete grammatical structure) bring new challenges to microblog text subjectivity classification task. The existing subjectivity classification approaches of microblog text mainly depend on the techniques of artificially constructing emotional features that are commonly based on the bag-of-words model, which will lead to the curse of dimensionality and data sparsity problem. Furthermore, microblog text usually contains the left-handed compliment or implicit representation. It is difficult to find and describe this implicit semantic representation by artificially constructing emotional features, which seriously affects the subjectivity classification accuracy. To address these problems, we introduce a deep recurrent convolution neural network model for subjectivity classification of microblog text with the multi-features combination. In our model, we apply the convolution neural networks to automatically learn classification features and use the recurrent neural networks to abstract and memorize the sequence semantic relations of microblog text. We also employ pre-trained word embedding to reduce the representation dimension of the text, which avoids dimension disasters and data sparsity issues. We perform experiments on six commonly used Twitter datasets. The experimental results demonstrate that our proposed model outperforms the state-of-the-art approaches on several datasets. Compared with the baseline methods, the accuracy and the average F1-measure of our proposed model for subjectivity classification are improved by a minimum of 1.21% and 2.82%, respectively.
Keyword:
Microblog
subjectivity classification
convolution neural network
recurrent neural network
deep learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Prognostic role of FUT8 expression in relation to p53 status in stage II and III colorectal cancer.png)
PLOS ONEOAAI
PLOS ONEIF0
OAAI分享收藏Enhancement of pyramid solar distiller performance using reflectors, cooling cycle, and dangled cords of wicksDesalination
DesalinationIF0
PREAI
分享收藏Evaluation of the Susceptibility of Several Plum (Prunus Domestica L.) Cultivars to Plum Pox Virus (Ppv) Infection in the Field. Sharka-Like Symptoms Observed on 'Jojo' Fruit are Not Related to PPV几种李属植物（Prunus Domestica L.）品种对李痘病毒（Ppv）田间感染易感性的评估。在'Jojo'果实上观察到的类似Sharka的症状与PPV无关。OAAI
OAAI分享收藏
查看更多内容