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【目的】分析电子商务中现有产品评价模式的不足,提出一种改进不足的产品评价新模式。【方法】在国内最大的微博平台上,针对某一产品主题抽取1 687条微博数据,并采用文本情感分类技术,对该样本数据集进行建模和分析。【结果】分析面向产品主题的微博数据,对其蕴含的语义信息进行归纳总结,发现其同样具有产品整体评价功能。并由于微博数据生成的自发性,其分析结果更具有客观性。【局限】更大规模样本数据的分析没有全面涉及,基于微博的动态产品评价研究没有涉及。【结论】该模式可以在一定程度上克服原有互联网产品评价模式的弱点,从而吸引更多企业关注微博产品评价信息。
【Objective】 To analyze the deficiency of existing product evaluation mode in e-commerce, and to propose a new mode of product evaluation with insufficient improvement. 【Method】 A total of 1 687 Weibo data were extracted from the theme of a product on the largest Weibo platform in China. The text data was classified and analyzed using text emotion classification technology. 【Result】 We analyzed the product-oriented Weibo data and summarized the semantic information contained in it. We found that it also has the overall product evaluation function. And because of the spontaneity generated by Weibo data, the analysis result is more objectivity. [Limitations] The analysis of larger-scale sample data is not fully covered. The research on dynamic product evaluation based on Weibo has not been studied. 【Conclusion】 This model can overcome the weakness of the original evaluation model of Internet products to a certain extent, and attract more enterprises to pay attention to the evaluation information of Weibo products.