结合新闻和评论文本的读者情绪分类方法 |
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引用本文: | 严倩,王礼敏,李寿山,周国栋. 结合新闻和评论文本的读者情绪分类方法[J]. 山东大学学报(理学版), 2018, 53(9): 35-39. DOI: 10.6040/j.issn.1671-9352.1.2017.003 |
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作者姓名: | 严倩 王礼敏 李寿山 周国栋 |
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作者单位: | 苏州大学自然语言处理实验室, 江苏 苏州 215006 |
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基金项目: | 国家自然科学基金资助项目(61331011,61672366,61375073) |
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摘 要: | 新闻和评论文本是进行读者情绪分类的重要资源,但仅仅使用新闻和文本或者把2类文本进行混合作为一组总体特征,不能充分利用不同文本特征间的区别和联系。基于此,提出了一种双通道LSTM(long short-term memory)方法,该方法把2类文本作为2组特征,分别用单通道LSTM神经网络学习这2组特征文本得到文本的LSTM表示,然后通过联合学习的方法学习这2组特征间的关系。实验结果表明,该方法能有效提高读者情绪的分类性能。
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关 键 词: | 联合学习 读者情绪分类 双通道LSTM |
收稿时间: | 2017-07-04 |
Reader emotion classification with news and comments |
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Affiliation: | Natural Language Processing Laboratory, Soochow University, Suzhou 215006, Jiangsu, China |
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Abstract: | The news and comments are important resources to classify the reader emotion. However, previous studies only used news texts or mixed two types of texts as a general feature, which did not make the best use of the differences and connections between different textual features. Based on it, the paper proposed a new approach named dual-channel LSTM, which treated two types of texts as different features. First, the approach learned a LSTM representation with a LSTM recurrent neural network. Then, it proposed a joint learning method to learn the relationship between the features. Empirical studies demonstrate the effectiveness of the proposed approach to reader emotion classification. |
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Keywords: | reader emotion classification joint learning dual-channel LSTM |
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