微信服务号

微信订阅号

2025-4-9- 9
Home > Archive>Volume 28, Issue 3, 2017 >676-693. DOI:10.13328/j.cnki.jos.005154
PDF HTML XML Export Cite reminder
Extracting Product Aspects and User Opinions Based on Semantic Constrained LDA Model
Author:
Affiliation:

Clc Number:

TP311

  • Article
    • | |
  • Metrics
    • |
  • Reference
    • |
  • Related [20]
    • |
  • Cited by
    • | |
  • Comments
    Abstract:

    With the development of online shopping, the Web has produced a large quantity of product reviews containing abundant evaluation knowledge about products. How to extract aspect and opinion words from the reviews and further obtain the sentiment polarity of the products at aspect level is the key problems to solve in fine-grained sentiment analysis of product reviews. First, considering certain features of Chinese product reviews, this paper designs methods to derive semantic relationships among words through syntactic analysis, word meaning understanding and context relevance, and then embed them as constrained knowledge into the topic model. Second, a semantic relation constrained topic model called SRC-LDA is proposed to guide the LDA to extract fine-grained topical words. Through the improvement of semantic comprehension and recognition ability of topical words in standard LDA, the proposed model can increase the words correlation under the same topic and the discrimination under the different topics, thus revealing more fine-grained aspect words, opinion words and their semantic associations. The experimental results show that SRC-LDA is an effective approach for fine-grained aspects and opinion words extraction.

    Reference
    Cited by
Get Citation

彭云,万常选,江腾蛟,刘德喜,刘喜平,廖国琼.基于语义约束LDA的商品特征和情感词提取.软件学报,2017,28(3):676-693

Copy
Share
Article Metrics
  • Abstract:2582
  • PDF: 5874
  • HTML: 2045
  • Cited by: 0
History
  • Received:July 03,2016
  • Revised:September 14,2016
  • Online: June 06,2018
Links:Institute of Software, CASChina Computer FederationCASNSFC
You are the first2034057Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address：4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code：100190
Phone：010-62562563 Fax：010-62562533 Email：jos@iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063