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

机译:谁说的...?识别专家与Layman Creics'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:我们通过构建二进制分类器来扩展经典审查挖掘工作,该分类预测了一份专家或外行书写的摘要文件的审查,精度(F1得分),并比较预测类的特征。各种标准的词汇和句法功能用于此监督的学习任务。我们的结果表明,专家编写了相对较长的繁忙和更详细的评论,其中包含更复杂的语法和更高的词汇量。外行评论是人民日常生活中更主观和上下情调化。我们的错误分析表明,作为专家的人可能比反之亦然大约是两倍的可能性。我们认为,作者的类型可能是提高预测评级,乐于助人和评论真实性的准确性的最有用的新功能。最后,这项工作的结果可能有助于研究人员和从业者在影响评估领域,以获得更加细微的了解对不同类型的媒体消费者和主题,流派或信息产品的审核人员的看法。

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