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A Joint Model for Chinese Microblog Sentiment Analysis

机译：中国微博情感分析的联合模型

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摘要

Topic-based sentiment analysis for Chinese microblog aims to identify the user attitude on specified topics. In this paper, we propose a joint model by incorporating Support Vector Machines (SVM) and deep neural network to improve the performance of sentiment analysis. Firstly, a SVM Classifier is constructed using N-gram, N-POS and sentiment lexicons features. Meanwhile, a convolutional neural network is applied to learn paragraph representation features as the input of another SVM classifier. The classification results outputted by these two classifiers are merged as the final classification results. The evaluations on the SIGHAN-8 Topic-based Chinese microblog sentiment analysis task show that our proposed approach achieves the second rank on micro average F1 and the fourth rank on macro average F1 among a total of 13 submitted systems.
机译：中国微博基于主题的情感分析旨在确定用户对特定主题的态度。在本文中，我们提出了一种结合支持向量机（SVM）和深度神经网络的联合模型，以提高情感分析的性能。首先，使用N-gram，N-POS和情感词典功能构建SVM分类器。同时，使用卷积神经网络来学习段落表示特征，作为另一个SVM分类器的输入。这两个分类器输出的分类结果将合并为最终分类结果。对基于SIGHAN-8主题的中国微博情感分析任务的评估表明，我们提出的方法在总共13个提交的系统中，在微平均值F1上排名第二，在宏平均值F1上排名第四。

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  • 来源
  • 会议地点 Beijing(CA)
  • 作者

    Yuhui Cao; Zhao Chen; Ruifeng Xu; Tao Chen; Lin Gui;

  • 作者单位

    Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China;

    Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China;

    Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China;

    Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China;

    Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China;

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