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Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective

机译：从性别角度看文本挖掘技术在eWOM通信中话语分析中的应用

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摘要

The emergence of online user-generated content has raised numerous questions about discourse gender differences as compared to face-to-face interactions. The intended gender-free equality of Internet has been challenged by numerous studies, and significant differences have been found in online communications. This paper proposes the application of text mining techniques to online gender discourse through the analysis of shared reviews in electronic word-of-mouth communities (eWOM), which is a form of user-generated content. More specifically, linguistic issues, sentiment analysis and content analysis were applied to online reviews from a gender perspective. The methodological approach includes gathering online reviews, pre-processing collected reviews and a statistical analysis of documents features to extract the differences between male and female discourses in a specific product category. Findings reveal not only the discourse differences between women and men but also their different preferences and the feasibility of predicting gender using a set of frequent key terms. These findings are interesting both for retailers so they can adapt their offer to the gender of customers, and for online recommender systems, as the proposed methodology can be used to predict the gender of users in those cases where the gender is not explicitly stated.
机译：与面对面的互动相比，在线用户生成的内容的出现引发了许多关于话语性别差异的问题。互联网的预期的无性别平等性已经受到众多研究的挑战，并且在在线交流中发现了显着差异。本文通过分析电子口碑社区（eWOM）中的共享评论，提出了文本挖掘技术在在线性别话语中的应用，eWOM是一种用户生成的内容。更具体地说，从性别角度出发，将语言问题，情感分析和内容分析应用于在线评论。该方法学方法包括收集在线评论，对收集的评论进行预处理以及对文档特征进行统计分析，以提取特定产品类别中男性话语和女性话语之间的差异。研究结果不仅揭示了男性和女性之间的话语差异，还揭示了他们的不同偏好以及使用一系列常用关键词预测性别的可行性。这些发现对于零售商来说都很有趣，因此他们可以根据客户的性别调整其报价，对于在线推荐系统，因为在没有明确说明性别的情况下，可以使用建议的方法来预测用户的性别。

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