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首页> 外文期刊>Frontiers of computer science in China >Learning from context: a mutual reinforcement model for Chinese microblog opinion retrieval
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Learning from context: a mutual reinforcement model for Chinese microblog opinion retrieval

机译：语境学习：中文微博舆情检索的互助模型

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摘要

This study addresses the problem of Chinese microblog opinion retrieval, which aims to retrieve opinionated Chinese microblog posts relevant to a target specified by a user query. Existing studies have shown that lexicon-based approaches employed online public sentiment resources to rank sentimentwords relying on the document features. However, this approach could not be effectively applied to microblogs that have typical user-generated content with valuable contextual information: "user-user" interpersonal interactions and "user-post/comment" intrapersonal interactions. This contextual information is very helpful in estimating the strength of sentiment words more accurately. In this study, we integrate the social contextual relationships among users, posts/comments, and sentiment words into a mutual reinforcement model and propose a unified three-layer heterogeneous graph, on which a random walk sentiment word weighting algorithm is presented to measure the strength of opinion of the sentiment words. Furthermore, the weights of sentiment words are incorporated into a lexicon-based model for Chinese microblog opinion retrieval. Comparative experiments are conducted on a Chinese microblog corpus, and the results show that our proposed mutual reinforcement model achieves significant improvement over previous methods.
机译：这项研究解决了中文微博观点检索的问题，该问题旨在检索与用户查询指定的目标相关的有意评论的中文微博帖子。现有研究表明，基于词典的方法利用在线公共情感资源来根据文档功能对情感词进行排名。但是，这种方法不能有效地应用于具有典型的用户生成的内容以及有价值的上下文信息的微博：“用户-用户”人际互动和“用户-帖子/评论”人际互动。此上下文信息对于更准确地估计情感词的强度很有帮助。在这项研究中，我们将用户，帖子/评论和情感词之间的社会上下文关系整合到一个相互增强的模型中，并提出了一个统一的三层异构图，在该图上提出了一种随机行走的情感词加权算法来衡量强度感言之见。此外，将情感词的权重结合到基于词典的中文微博意见检索模型中。在中文微博语料库上进行了比较实验，结果表明我们提出的互增强模型比以前的方法有了显着改进。

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