有毒审查内容对总产品进量的影响 (Effect of Toxic Review Content on Overall Product Sentiment)

Toxic contents in online product review are a common phenomenon. A content is perceived to be toxic when it is rude, disrespectful, or unreasonable and make individuals leave the discussion. Machine learning algorithms helps the sell side community to identify such toxic patterns and eventually moderate such inputs. Yet, the extant literature provides fewer information about the sentiment of a prospective consumer on the perception of a product after being exposed to such toxic review content. In this study, we collect a balanced data set of review comments from 18 different players segregated into three different sectors from google play-store. Then we calculate the sentence-level sentiment and toxicity score of individual review content. Finally, we use structural equation modelling to quantitatively study the influence of toxic content on overall product sentiment. We observe that comment toxicity negatively influences overall product sentiment but do not exhibit a mediating effect over reviewer score to influence sector-wise relative rating.

翻译：网上产品审查中的有毒内容是一种常见现象。当内容粗鲁、不尊重或不合理时,就被视为有毒内容,使个人退出讨论。机器学习算法帮助销售方社区识别此类有毒模式,并最终调节此类投入。然而,现有文献对潜在消费者在接触此类有毒产品审查内容后对产品感知的情绪提供的信息较少。在本研究中,我们收集了18个不同玩家的均衡的一组审查评论数据集,这些评论分为与谷歌游戏场不同的三个部门。然后,我们计算了个人审查内容的判刑程度和毒性得分。最后,我们利用结构等式模型进行定量研究,研究有毒内容对整个产品感的影响。我们发现,评论毒性对产品总体感觉有负面影响,但不会对审评人的评分产生影响部门相对评级的中间效应。