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网购评论情感数据的k近邻和SVM处理方法研究 被引量：3

Research on online-shopping sentiment based on k-nearest neighbor and SVM algorithm
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摘要 针对网购评论,抽取评论语组成基本语料,构建客户网购评论情感词汇本体,对热点评论应用k-近邻和SVM 2种算法来分析评论文本热点事件,实验证实SVM算法较k-近邻算法在评论文本热点发现上具有较高的性能,为网购评语热点研究提供了实例参考. Based on the basic corpus composed of comment extracts from online - shopping, the thesis builds a voabulary database of customers＇ attitudes towards the online - shopping comments. The comments on the hot events are analyzed by applying k - nearest neighbor and SVM algorithm. The result shows that SVM algorithm is better than k -nearest in this aspect, which provides reference for studying on the online -shopping comments.
作者 焦畅 佘玉梅 丁冬冬 庄丽
机构地区 云南民族大学数学与计算机科学学院
出处 《云南民族大学学报（自然科学版）》 CAS 2013年第3期209-212,共4页 Journal of Yunnan Minzu University:Natural Sciences Edition
基金 国家自然科学基金(08XMZ002)
关键词 网购评论 K近邻 SVM算法 意见挖掘 online -shopping comments k -nearest neighbor SVM algorithm opinion mining
分类号 TP393 [自动化与计算机技术—计算机应用技术]
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参考文献10

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二级参考文献43

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共引文献131

同被引文献19

引证文献3

二级引证文献3

云南民族大学学报（自然科学版）
2013年 第3期

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