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Says Who...? Identification of Expert versus Layman Critics' Reviews of Documentary Films

机译:谁说的...?确定专家与Layman评论家对纪录片的评论

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We extend classic review mining work by building a binary classifier that predicts whether a review of a documentary film was written by an expert or a layman with 90.70% accuracy (F1 score), and compare the characteristics of the predicted classes. A variety of standard lexical and syntactic features was used for this supervised learning task. Our results suggest that experts write comparatively lengthier and more detailed reviews that feature more complex grammar and a higher diversity in their vocabulary. Layman reviews are more subjective and contextualized in peoples' everyday lives. Our error analysis shows that laymen are about twice as likely to be mistaken as experts than vice versa. We argue that the type of author might be a useful new feature for improving the accuracy of predicting the rating, helpfulness and authenticity of reviews. Finally, the outcomes of this work might help researchers and practitioners in the field of impact assessment to gain a more fine-grained understanding of the perception of different types of media consumers and reviewers of a topic, genre or information product.
机译:我们通过建立一个二进制分类器来扩展经典评论的挖掘工作,该分类器可预测纪录片的评论是由专家还是非专业人员以90.70%的准确性(F1分数)撰写的,并比较预测类的特征。各种标准的词汇和句法功能都用于此受监督的学习任务。我们的结果表明,专家撰写的文章相对较长和更详细,其语法更复杂,词汇量也更高。在人们的日常生活中,外行评论更加主观和具体。我们的错误分析表明,外行被误解的可能性是专家的两倍,反之亦然。我们认为作者的类型可能是有用的新功能,可以提高预测评论的等级,有用性和真实性的准确性。最后,这项工作的结果可能会帮助影响评估领域的研究人员和从业人员更深入地了解不同类型的媒体消费者和主题,体裁或信息产品的审阅者的看法。

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