| 基于Attention-CLSTM模型的商品评论分类 |
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| 引用本文： | 张 鹏,张再跃.基于Attention-CLSTM模型的商品评论分类[J].教育技术导刊,2020,19(2):84-87. |
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| 作者姓名： | 张 鹏 张再跃 |
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| 作者单位： | 江苏科技大学 计算机学院，江苏 镇江 212003 |
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| 基金项目： | 国家自然科学基金项目（61371114，611170165）；江苏高校高技术船舶协同创新中心/江苏科技大学海洋装备研究院项目（1174871701-9） |
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| 摘 要： | 文本分类是自然语言处理中的一项重要基础任务，指对文本集按照一定的分类体系或标准进行自动分类标记。目前网络文化监督力度不够、不当言论不受限制，导致垃圾评论影响用户体验。因此提出一种基于注意力机制的CLSTM混合神经网络模型，该模型可以快速有效地区分正常评论与垃圾评论。将传统机器学习SVM模型和深度学习LSTM模型进行对比实验，结果发现，混合模型可在时间复杂度上选择最短时间，同时引入相当少的噪声，最大化地提取上下文信息，大幅提高评论短文本分类效率。对比单模型分类结果，基于注意力机制的CLSTM混合神经网络模型在准确率和召回率上均有提高。
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| 关 键 词： | 文本分类 深度学习 注意力机制 CLSTM |
| 收稿时间： | 2019-04-26 |
Classification of Commodity Reviews Based on Attention-CLSTM Model |
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| ZHANG Peng,ZHANG Zai-yue.Classification of Commodity Reviews Based on Attention-CLSTM Model[J].Introduction of Educational Technology,2020,19(2):84-87. |
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| Authors: | ZHANG Peng ZHANG Zai-yue |
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| Institution: | School of Computer Science and Engineering， Jiangsu University of Science and Technology， Zhenjiang 212003， China |
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| Abstract: | Text classification is an important basic task in natural language processing， which automatically classifies text sets according to certain classification systems or standards. Problems prevail because of current lack of supervision of the network culture and restriction of improper comments， which leads to the problem of spam comments affecting the user experience. In this paper， a CLSTM hybrid neural network model based on attention mechanism is used to classify commodity reviews， which can quickly and effectively distinguish normal comments from spam comments. At the same time， compared with the traditional machine learning SVM model and the deep learning LSTM model， it is found that the hybrid model can select the shortest time in time complexity， introduce relatively little noise， and maximize the extraction of context information in the comments. The effect of short text classification has been significantly improved. Compared with the single model classification results， the CLSTM used in this paper has improved both accuracy and recall rate. |
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| Keywords: | text classification deep learning attention mechanism CLSTM |
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