基于朴素贝叶斯的细粒度意见挖掘
摘要
针对常用细粒度意见挖掘模型条件随机场(CRF)需要大量细致的标注语料,费时费力,提出基于朴素贝叶斯的细粒度意见挖掘方法。该方法在朴素贝叶斯的基础上融合多种语言特征,对产品评论进行细粒度意见挖掘,提取评论文本中的评价要素,既避免了大量标注数据,省时省力,又增加了分类特征,提高分类精度。实验结果表明,评价要素识别的综合准确率达82%左右,比起常用模型,不但效率提高了,准确率也有所提高。
引文
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[10]陈炳丰,郝志峰,蔡瑞初,等.面向汽车评论的细粒度情感分析方法研究[J].广东工业大学学报,2017,34(3):8-14.
[11]张玥.面向产品评价的细粒度情感分析技术研究[D].哈尔滨:哈尔滨工业大学,2013:21-27.
[12]刘丽,王永恒,韦航.面向产品评论的细粒度情感分析[J].计算机应用,2015,35(12):3481-3486.
[2]SOHANGIR S,WANG D,POMERANETS A,et al.Big data:deep learning for financial sentiment analysis[J].Journal of big data,2018,5(1):1-25.
[3]林江豪,阳爱民,周咏梅,等.一种基于朴素贝叶斯的微博情感分类[J].计算机工程与科学,2012,34(9):160-165.
[4]GAMON M,AUE A,CORSTON-OLIVER S,et al.Pulse:Mining Customer Opinions from Free Text[C]//Proceedings of the 6th International Conference on Advances in Intelligent Data Analysis.Madrid,Spain:Springer-Verlag,2005:121-132.
[5]HU M,LIU B.Mining and Summarizing Customer Reviews[C]//Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Seattle,Washington,USA:ACM Press,2004:168-177.
[6]POPESCU A M,ETZIONI O.Extracting Product Features and Opinions from Reviews[C]//Natural Language Processing and Text Mining.London:Springer,2007:9-28.
[7]JIN W,HO H H.A Novel Lexicalized HMM-based Learning Framework for Web Opinion Mining[C]//Proceedings of the 26th Annual International Conference on Machine Learning.New York:ACM,2009:465-472.
[8]赵文婧.产品描述词及情感词抽取模式的研究[D].北京:北京邮电大学,2010.
[9]TANG D Y,QIN B,LIU T.Deep learning for sentiment analysis:successful approaches and future challenges[M].New York:John Wiley&Sons,Inc.,2015.
[10]陈炳丰,郝志峰,蔡瑞初,等.面向汽车评论的细粒度情感分析方法研究[J].广东工业大学学报,2017,34(3):8-14.
[11]张玥.面向产品评价的细粒度情感分析技术研究[D].哈尔滨:哈尔滨工业大学,2013:21-27.
[12]刘丽,王永恒,韦航.面向产品评论的细粒度情感分析[J].计算机应用,2015,35(12):3481-3486.