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首页> 外文会议>Workshop on computational approaches to subjectivity, sentiment and social media analysis >DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning
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DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning

机译：DataSearch在iest 2018：嵌入基于Word的模型，了解深度学习的推文的隐含情绪分类

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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.
机译：本文介绍了一种解决隐式情绪分类的方法，利用预先训练的单词嵌入模型来训练多个神经网络。本文描述的系统由用于分类的特征提取和前馈神经网络的长短期存储器和卷积神经网络的连续组合组成。在本文中，我们成功地显示使用多个预先培训的嵌入式提取的功能可用于提高系统的整体性能，以表达emoji是其中一个重要特征之一。评估表明，无需使用任何外部语料库或词典，我们的方法在8％以上超过了8％。在WASE A-2018上的隐含情绪共享任务（IEST）中，这种方法排名第8。

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