This paper aims to investigate the relationship between sentiment and review helpfulness and develop a method to fully use sentiment features in review helpfulness assessment. In addition, this paper explores whether product type influences evaluating review helpfulness.,First, a high-quality data set with a manually coded helpfulness score was constructed. Second, detailed research question methods were conducted. Finally, methods were applied to the data set to extract information gain and sentiment scores. Gradient boosting and random forest methods were used to classify the data set with these features through recall, precision and F-measure to understand the research questions.,Review sentiment has a deep relationship with review helpfulness, and it can be a strong predictor of review helpfulness by refining it into more detailed scores; a combination of sentiment scores and information gain works very well on classification for two product types. Product type does not show a significant influence on helpfulness assessment.,This paper provides a different perspective for measuring review sentiment by clarifying the relationship between sentiment and review helpfulness, analysing the role of product type in review helpfulness assessment, and proposing a high-value feature combination. In addition, the author believes that the assessment method can be effectively applied to practical works.

中文翻译：

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基于情感分析的用户评论有用性评估

本文旨在研究情感与评论有用性之间的关系，并开发一种在评论有用性评估中充分利用情感特征的方法。此外，本文探讨了产品类型是否影响评价评论有用性。首先，构建了一个具有手动编码有用性评分的高质量数据集。其次，进行了详细的研究问题方法。最后，将方法应用于数据集以提取信息增益和情感分数。使用梯度提升和随机森林方法通过召回率、精度和F-measure对具有这些特征的数据集进行分类，以理解研究问题。评论情绪与评论有用性有很深的关系，它可以作为评论的强预测因子通过将其细化为更详细的分数来提供帮助；情感分数和信息增益的组合在两种产品类型的分类上效果很好。产品类型对有用性评估没有显着影响。,本文通过阐明情感和评论有用性之间的关系，分析产品类型在评论有用性评估中的作用，并提出一个高价值的评论情感测量方法。特征组合。此外，笔者认为该评价方法可以有效地应用于实际工作中。本文通过阐明情感与评论有用性之间的关系，分析产品类型在评论有用性评估中的作用，并提出高价值特征组合，为衡量评论情感提供了不同的视角。此外，笔者认为该评价方法可以有效地应用于实际工作中。本文通过阐明情感与评论有用性之间的关系，分析产品类型在评论有用性评估中的作用，并提出高价值特征组合，为衡量评论情感提供了不同的视角。此外，笔者认为该评价方法可以有效地应用于实际工作中。