梳理产品评论中24类信息类型，助力理解评论价值与意图
Information Types in Product Reviews
- 构建24类沟通目标的分类体系，支持零样本多标签识别
- 信息类型组合能预测评论有用性和情感倾向
- 适用于分析评论意图、效果及修辞结构，适合数据驱动研究者
文本中的信息以服务于读者目标的方式传达。例如，产品评论包含意见、建议、产品描述等多种信息类型，不仅提供直接洞察，还为下游应用提供意外信号。本文提出一个涵盖24种交际目标的分类体系，并采用零样本多标签分类器，实现对评论数据的大规模分析。实验表明，该分类体系中的类别组合可有效预测评论的有用性与情感倾向，同时提供决策解释。此外，该分类体系还能用于分析评论的意图、有效性及修辞结构。对评论中信息类型的刻画，为更高效地利用这一文体提供了诸多可能。
原文摘要 · Abstract (English)
Information in text is communicated in a way that supports a goal for its reader. Product reviews, for example, contain opinions, tips, product descriptions, and many other types of information that provide both direct insights, as well as unexpected signals for downstream applications. We devise a typology of 24 communicative goals in sentences from the product review domain, and employ a zero-shot multi-label classifier that facilitates large-scale analyses of review data. In our experiments, we find that the combination of classes in the typology forecasts helpfulness and sentiment of reviews, while supplying explanations for these decisions. In addition, our typology enables analysis of review intent, effectiveness and rhetorical structure. Characterizing the types of information in reviews unlocks many opportunities for more effective consumption of this genre.
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