[1]唐 莉,刘 臣.基于 CRF 和 HITS 算法的特征情感对提取[J].计算机技术与发展,2019,29(07):71-75.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 014]
　TANG Li,LIU Chen.Extraction of Feature and Sentiment Word Pair Based on Conditional Random Fields and HITS Algorithm[J].,2019,29(07):71-75.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 014]
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基于 CRF 和 HITS 算法的特征情感对提取()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年07期
页码:
71-75
栏目:
智能、算法、系统工程
出版日期:
2019-07-10

文章信息/Info

Title:
Extraction of Feature and Sentiment Word Pair Based on Conditional Random Fields and HITS Algorithm
文章编号:
1673-629X(2019)07-0071-05
作者:
唐 莉; 刘 臣
上海理工大学,上海 200093
Author(s):
TANG Li; LIU Chen
University of Shanghai for Science and Technology,Shanghai 200093,China
关键词:
条件随机场; 情感分析; 依存句法分析; 二分网; 扩展 HITS 算法
Keywords:
conditional random field; sentiment analysis; dependency parsing; bipartite network; extended HITS algorithm
分类号:
TP391.1
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 07. 014
摘要:
作为情感分析的子任务之一,特征级情感分析备受关注。 条件随机场(CRF)是情感分析任务的常用方法之一,特别是对于产品特征的提取,但是针对特征词与情感词之间的长依存问题难以解决。 针对该问题,提出一种基于 CRF 和 HITS 算法的两阶段方法来提取(产品特征-情感词)对。 使用 CRF 并利用词、词性、依存句法关系三种文本特征来对产品评论中的评价特征和情感词进行提取,并利用已提取的特征和情感词分别作为权威节点和枢纽节点来构建特征情感词二分网。 使用一种称为 MHITS 的扩展 HITS 算法在二分网上计算并对(产品特征-情感词)对进行排序。 实验使用了京东平台上三种不同类型产品的评论数据,并与基准方法进行比较,结果表明该模型在准确率、召回率和 F 1 值上表现更平均。
Abstract:
As one of the sub-tasks of sentiment analysis,feature-level sentiment analysis has attracted much more attention. In the past,conditional random field (CRF) was one of the commonly used methods for sentiment analysis tasks,especially for feature extraction. However,it was difficult for this method to solve the long-range dependence problem between feature words and sentiment words. Therefore,a two-stage method based on CRF and HITS algorithm is proposed for extracting the pair of product feature-sentiment word. CRF and the three kinds of text features,including word,part of speech and dependency parsing,are utilized to extract features and sentiment words. These features and sentiment words are taken as the authority node and the hub node respectively to constitute a bipartite feature-sentiment relation network. An extended HITS algorithm called MHITS is applied to calculate and sort the features and sentiment word pairs on a bipartite network. The experiment uses the reviews over three different types of products on Jingdong platform. Compared with the benchmark method,the results show that the model performs more evenly in terms of precision,recall and F 1 measure.

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更新日期/Last Update: 2019-07-10