Cross-domain comparison of algorithm performance in extracting aspect-based opinions from Chinese online reviews
Cross-domain comparison of algorithm performance in extracting aspect-based opinions from Chinese online reviews
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DOI：
10.1007/s13042-016-0596-x
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发表时间：
2016-09
影响因子：
5.6
通讯作者：
Wei Wang;Guanyin Tan;Hongwei Wang
中科院分区：
文献类型：
--
作者：
Wei Wang;Guanyin Tan;Hongwei Wang
抽取方面和观点是进行细粒度情感分析的基础。它通常以以下两种方式之一进行：基于规则的方法和机器学习方法。然而，目前还没有关于中文多领域适用性的结论，所以这些算法的健壮性和可靠性受到了不同领域的关注。我们从7个领域对10种基于中文语料库的体观点抽取方法进行了比较。比较的方法包括基于TF的模型+词性挖掘、基于CRFS的观点挖掘、基于支持向量机的观点挖掘、基于MNB的观点挖掘、基于HMM的观点挖掘、基于RFM的观点挖掘、基于RNN的观点挖掘、基于KNN的观点挖掘、基于CART的观点挖掘和基于LPM的观点挖掘。我们收集了3146条中文评论作为语料库，包括数码相机、化妆品、书籍、酒店、电影、手机和餐馆。实验结果表明：(1)没有一种算法在所有领域都具有优势；(2)机器学习算法优于基于规则的方法；(3)对于基于规则的方法，文本长度对意见挖掘的准确率有负面影响，而一些机器学习方法擅长提取长评论；(4)对于基于HMM的模型、基于RFM的模型、基于RNN的模型、基于KNN的模型、基于CART的模型和基于LPM的模型，在准确率和召回率方面，基于支持向量机的方法是几乎所有领域中最好的。
Extracting aspects and opinions is the basis of sentiment analysis in fine-grained manner. It is often conducted in one of the following two ways: rule-based approaches and machine learning approaches. However, no conclusion has been drawn yet on the matter of multi-domains applicability in Chinese, so robustness and reliability across different fields are being of concern to these algorithms. We compare ten approaches of aspect-opinion extraction on Chinese corpora from seven domains. The compared methods include TF-based model plus POS, CRFs-based opinion mining, SVM-based opinion mining, MNB-based opinion mining, HMM-based opinion mining, RFM-based opinion mining, RNN-based opinion mining, KNN-based opinion mining, CART-based opinion mining and LPM-based opinion mining. We collect 3146 Chinese reviews as corpora including digital camera, cosmetics, book, hotel, movie, cellphone and restaurant. Experiments reveal the following results: (1) no algorithm dominates over all domains, (2) machine learning algorithms outperform rule-based approaches, (3) the length of text affects the accuracy of opinion mining negatively for rule-based approaches, while some machine learning methods are good at extracting long reviews, (4) for HMM-based model, RFM-based model, RNN-based model, KNN-based model, CART-based model and LPM-based model, the performances are similar in terms of precision and recall, (5) overall, SVM-based approach performs best among almost all the domains for opinion mining.