论文标题
有毒审查内容对整体产品情感的影响
Effect of Toxic Review Content on Overall Product Sentiment
论文作者
论文摘要
在线产品评论中的有毒内容是一种常见现象。当内容是粗鲁，不尊重或不合理的情况下，内容被认为是有毒的，并使个人离开讨论。机器学习算法可以帮助卖方社区确定这种有毒模式，并最终适应此类投入。然而，现有的文献提供了有关潜在消费者对产品感知到这种有毒审查内容后对产品感知的情感的更少信息。在这项研究中，我们收集了来自18个不同玩家的评论评论的平衡数据集，这些评论将隔离为Google Play商店的三个不同部门。然后，我们计算单个审查内容的句子级别的情感和毒性评分。最后，我们使用结构方程建模来定量研究有毒含量对整体产品情感的影响。我们观察到，评论毒性会对整体产品情绪产生负面影响，但没有对审查者评分表现出中介作用，从而影响扇区的相对评级。
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.
