融入情感信息词向量的评论文本情感分析方法
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河北省创新能力提升计划项目(19456003D)


Sentiment analysis method of comment text based on word vector with sentiment information
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    摘要:

    为了解决分布式词表示方法因忽略词语情感信息导致情感分类准确率较低的问题,提出了一种融入情感信息加权词向量的情感分析改进方法。依据专属领域情感词典构建方法,结合词典和语义规则,将情感信息融入到TF-IDF算法中,利用Word2vec模型得到加权词向量表示方法,并运用此方法对采集到的河北省旅游景点的评论文本与对照组进行对比实验。结果表明,与基于分布式词向量表示的情感分析方法相比,采用融入情感信息加权词向量的改进方法进行情感分析,积极文本的准确率提高了6.1%,召回率提高了6.6%,F值达到了90.3%;消极评论文本的准确率提高了6.0%,召回率提高了7.2%,F值达到了89.6%。因此,融入情感信息加权词向量的情感分析改进方法可以有效提高评论文本情感分析的准确率,为用户获得更为准确的评论观点提供参考。

    Abstract:

    In order to solve the problem of low accuracy of sentiment classification caused by neglecting the sentiment information of words in distributed word representation method,an improved sentiment analysis method incorporating weighted word vectors of sentiment information was proposed.According to the exclusive domain sentiment dictionary,combined with the dictionary and semantic rules,the sentiment information is integrated into the TF-IDF algorithm,and the weighted word vector representation method is obtained by using word2vec model.The method is used to compare the collected comments of tourist attractions in Hebei Province with the control group.The results show that compared with the sentiment analysis method based on distributed word vector representation,the accuracy and recall rate of positive text are increased by 61% and 66%,and the F value reached 903%,the accuracy and recall rate of negative text are increased by 60% and 72%,and the F value reached 896% by using the improved method of sentiment analysis integrated with sentiment information weighted word vector.Therefore,the improved method of sentiment analysis integrated with sentiment information weighted word vector can effectively improve the accuracy of sentiment analysis of comment text,and provide valuable reference for users to obtain more accurate comments.

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吕妹园,张永健,张永强,孙胜娟.融入情感信息词向量的评论文本情感分析方法[J].河北科技大学学报,2021,42(4):380-388

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  • 收稿日期:2021-03-25
  • 最后修改日期:2021-06-11
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  • 在线发布日期: 2021-08-26
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