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Commodity feature extraction for Chinese online comments based on semantic

机译：基于语义的中文在线评论商品特征提取

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摘要

Opinion mining based on Chinese online comments has been widely concerned, and its goal is to analyze user's attitude towards commodities' features from massive online comments. Commodity feature extraction is the basis of opinion mining. Most existing commodity feature extraction methods cannot achieve cooperating analysis of semantic rules and commodity feature extraction, or applying statistical methods to extract the commodity features, the feature seed weights are not reasonable, or the thresholds of the candidate features are not reasonable. For these reasons, this paper proposes a method to extract commodity feature based on Chinese online comments. In this method, the semantic rules of commodity feature are more perfectly defined, which significantly reduces the noise of candidate feature set, and simplifies the subsequent features' extraction. In this paper, we also set the seed weight reasonably, and obtain the changed threshold and seed set by iterative method. Experimental results show that the proposed method can effectively and quickly perform unsupervised learning. Moreover, compared with other methods, it has better recognition performance.
机译：基于中文在线评论的观点挖掘一直受到广泛关注，其目标是从大量在线评论中分析用户对商品功能的态度。商品特征提取是观点挖掘的基础。现有的大多数商品特征提取方法无法实现语义规则与商品特征提取的协同分析，也无法应用统计方法提取商品特征，特征种子权重不合理，候选特征阈值不合理。基于这些原因，本文提出了一种基于中文在线评论的商品特征提取方法。该方法可以更好地定义商品特征的语义规则，从而大大降低了候选特征集的噪声，简化了后续特征的提取。在本文中，我们还合理地设置了种子权重，并通过迭代的方法获得了变化的阈值和种子集。实验结果表明，该方法可以有效，快速地进行无监督学习。而且，与其他方法相比，它具有更好的识别性能。

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