DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning 机翻标题: 暂无翻译,请尝试点击翻译按钮。

会议集名/来源
9th workshop on computational approaches to subjectivity, sentiment and social media analysis: 9th workshop on computational approaches to subjectivity, sentiment and social media analysis (WASSA 2018), 31 October 2018, Brussels, Belgium
出版年
2018
页码
211-216
会议地点
Brussels
作者单位
University of Moratuwa, Sri LankaUniversity of Moratuwa, Sri Lanka
作者
Yasas Senarath;Uthayasanker Thayasivam
摘要
This paper describes an approach to solve implicit emotion classification with the use of pre-trained word embedding models to train multiple neural networks. The system described in this paper is composed of a sequential combination of Long Short-Term Memory and Convolutional Neural Network for feature extraction and Feedforward Neural Network for classification. In this paper, we successfully show that features extracted using multiple pre-trained embeddings can be used to improve the overall performance of the system with Emoji being one of the significant features. The evaluations show that our approach outperforms the baseline system by more than 8% without using any external corpus or lexicon. This approach is ranked 8th in Implicit Emotion Shared Task (IEST) at WASS A-2018.
机翻摘要
暂无翻译结果,您可以尝试点击头部的翻译按钮。
若您需要申请原文,请登录。

最新评论

暂无评论。

登录后可以发表评论

意见反馈
返回顶部