[目的/意义] 提出一种以情感加权算法和朴素贝叶斯算法为基础的组合分类模型（SWNB模型），旨在对中文微博话题的立场进行判别。[方法/过程] 该模型首先通过给定的复杂句模型对微博进行简化，然后依据情感规则得到情感权值，提取微博中与话题相关的实体并进行优化，进而将微博分为包含立场和未表明立场（NONE）两类；再对包含立场的微博提取特征词，利用朴素贝叶斯算法将其立场判别为支持（FAVOR）或反对（AGAINST）。[结果/结论] 实验结果表明，本模型有较好的立场判别精度，并能同时有效地处理中文复杂句式、话题相关评价对象以及上下文语境等复杂情形。

[Purpose/significance] The paper introduces a new approach to automatically detect stance in Chinese microblogs by building a serial combination model based on Sentiment Weighted Algorithm and Naive Bayes (SWNB model). [Method/process] Firstly, this paper used the SWNB model to simplify complex sentences by using a defined complex sentence pattern, assigning a sentiment weight to each microblog according to calculation rules, and optimizing sentiment weight by detecting the presence of the target’s associated entities; thus, we could classify microblogs into those containing any stance or with no stance at all. Secondly, the SWNB model extracted some feature words and used Naive Bayes to classify the microblogs labeled as FAVOR or AGAINST. [Result/conclusion] Experiments show that this model can comprehensively process complex sentences, target-related entities and linguistic context.