基于深度CNN的面向方面层次意见分析的方面提取

Ali Alemi Matin Pour, S. Jalili
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引用次数: 3

摘要

方面项的提取是方面级情感分析的关键;情感分析收集和提取社交媒体和网站评论中表达的观点,然后进行分析,帮助用户和利益相关者更好、更快地了解公众对所提出问题的看法。方面级情感分析提供了更详细的信息,这对许多不同领域的使用非常有益。本文的重要贡献是提供了一种数据预处理方法和一种深度卷积神经网络(CNN),将自以为是句子中的每个词标记为方面或非方面词。该方法提取出可用于分析评论和意见中所表达的方面术语的情感的方面术语。在SemEval-2014数据集上进行的实验结果表明,该方法的性能优于其他著名的方法,如深度CNN。本文提出的基于深度CNN网络的数据预处理方法在餐厅和笔记本电脑领域根据F-measure提取方面项的效率分别提高了1.05%和0.95%。
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Aspects Extraction for Aspect Level Opinion Analysis Based on Deep CNN
Extracting aspect term is essential for aspect level sentiment analysis; Sentiment analysis collects and extracts the opinions expressed in social media and websites' comments and then analyzes them, helping users and stakeholders understand public views on the issues raised better and more quickly. Aspect-level sentiment analysis provides more detailed information, which is very beneficial for use in many various domains. In this paper, the significant contribution is to provide a data preprocessing method and a deep convolutional neural network (CNN) to label each word in opinionated sentences as an aspect or non-aspect word. The proposed method extracts the terms of the aspect that can be used in analyzing the sentiment of the expressed aspect terms in the comments and opinions. The experimental results of the proposed method performed on the SemEval-2014 dataset show that it performs better than other prominent methods such as deep CNN. The proposed data preprocessing method with the deep CNN network can improve extraction of aspect terms according to F-measure by at least 1.05% and 0.95% on restaurant and laptop domains.
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