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Summarising customer online reviews using a new text mining approach

机译:使用新的文本挖掘方法汇总客户在线评论

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摘要

In recent years,with the expansion of electronic commerce,the number of customer online reviews available on the internet is growing rapidly.Lots of online merchant's websites ask the customers to leave a review about their experiences with the products.The reviews gathered from these websites are rich source of information for product development and marketing.The large volume of reviews that a product receives,make it hard for a potential customer or a manufacturer to read them and know about the customers' preferences,needs and experiences.So,this large volume of text data needs to be summarised using text mining approaches.The approach used in this paper to overcome this problem,is to develop a text summarisation system which extracts and groups the representative sentences of customer reviews.The proposed system,first extracts key topics discussed frequently in the customer review texts in the form of sequences of words.Then,the proposed system,groups the sentences assigned to the key topics,based on their semantic and syntactic similarity,using a genetic clustering algorithm.The evaluation result of the proposed system shows that the technique is effective and outperforms an existing text summarisation method.
机译:近年来,随着电子商务的发展,互联网上可用的客户在线评论数量迅速增长。许多在线商人网站要求客户对他们的产品使用体验进行评论。这些评论是从这些网站收集的是产品开发和市场营销的丰富信息来源。产品收到的大量评论使潜在客户或制造商难以阅读它们并了解客户的偏好,需求和经验。需要使用文本挖掘方法来汇总大量文本数据。本文用于解决此问题的方法是开发一种文本摘要系统,该系统可以提取并分组客户评论的代表性句子。所提出的系统首先提取关键主题在客户评论文本中经常以单词序列的形式进行讨论。然后,提议的系统将分配给客户的句子分组该系统的评估结果表明,该方法是有效的,并且优于现有的文本摘要方法。

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