《計算機應用研究》|Application Research of Computers

基于改進SimRank的產品特征聚類研究

Product feature clustering based on improved SimRank

免費全文下載 (已被下載 次)  
獲取PDF全文
作者 劉臣,段俊
機構 上海理工大學 管理學院,上海 200093
統計 摘要被查看 次,已被下載
文章編號 1001-3695(2019)07-007-1951-04
DOI 10.19734/j.issn.1001-3695.2018.01.0027
摘要 針對在線用戶評論中產品特征的提取和聚類問題進行了研究,提出一種改進的SimRank算法。將情感詞—特征對放入二分網中,在二分網中使用改進后的SimRank算法計算特征詞之間的相似度;再通過譜聚類算法對特征相似度進行聚類,提取網絡產品的特征集合。以某電腦評論為例,從中提取情感詞—特征對進行研究。實驗結果顯示,改進后的算法準確率更高。改進后的特征相似度檢測方法可以作為檢測特征相似度的有效方法,實驗采用在線產品的評論語料。實驗結果表明,使用改進后的SimRank相似度對特征詞進行聚類提取出特征更加準確。
關鍵詞 SimRank算法; 特征聚類; 二分網; 特征相似度
基金項目 國家自然科學基金資助項目(71401107,71774111)
本文URL http://www.oirznw.live/article/01-2019-07-007.html
英文標題 Product feature clustering based on improved SimRank
作者英文名 Liu Chen, Duan Jun
機構英文名 Business School,University of Shanghai for Science & Technology,Shanghai 200093,China
英文摘要 This paper studied the extraction and clustering of product features in online user reviews. It proposed an improved SimRank algorithm to put the affective word-feature pair into the binary network. And it used the improved SimRank algorithm to compute the similarity between the characteristic words. Then it adopted the spectral clustering algorithm to cluster the feature similarity. Extracts feature sets for network products. Taking a computer commentary as an example, this paper extracted affective word-feature pairs. The experimental results show that the improved algorithm has higher accuracy. The improved feature similarity detection method can be used as an effective method for detecting feature similarity. The experimental results show that using the improved SimRank similarity to extract the feature words is more accurate.
英文關鍵詞 SimRank algorithm; feature clustering; binary network; feature similarity
參考文獻 查看稿件參考文獻
 
收稿日期 2018/1/17
修回日期 2018/3/8
頁碼 1951-1954
中圖分類號 TP391
文獻標志碼 A
水果机返水