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Commodity feature extraction for Chinese Online Comments Based on Semantic

机译：基于语义的中国在线评论的商品特征提取

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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：基于中国在线评论的意见采矿已被广泛关注，其目标是分析用户对大规模在线评论的商品的态度。商品特征提取是意见采矿的基础。大多数现有的商品特征提取方法无法实现语义规则和商品特征提取的协作分析，或应用统计方法提取商品特征，特征种子重量不合理，或者候选功能的阈值不合理。由于这些原因，本文提出了一种基于中文在线评论提取商品功能的方法。在这种方法中，商品特征的语义规则更完全定义，这显着降低了候选功能集的噪声，并简化了随后的特征提取。在本文中，我们还合理地设置了种子重量，并通过迭代方法获得改变的阈值和种子。实验结果表明，该方法可以有效，快速地进行无监督的学习。此外，与其他方法相比，它具有更好的识别性能。

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