Purpose

The evaluation of perceived attribute performance reflected in online consumer reviews (OCRs) is critical in gaining timely marketing insights. This study proposed a text mining approach to measure consumer sentiments at the feature level and their asymmetric impacts on overall product ratings.

Design/methodology/approach

This study employed 49,130 OCRs generated for 14 wireless earbud products on Amazon.com. Word combinations of the major quality dimensions and related sentiment words were identified using bigram natural language processing (NLP) analysis. This study combined sentiment dictionaries and feature-related bigrams and measured feature level sentiment scores in a review. Furthermore, the authors examined the effect of feature level sentiment on product ratings.

Findings

The results indicate that customer sentiment for product features measured from text reviews significantly and asymmetrically affects the overall rating. Building upon the three-factor theory of customer satisfaction, the key quality dimensions of wireless earbuds are categorized into basic, excitement and performance factors.

Originality/value

This study provides a novel approach to assess customer feature level evaluation of a product and its impact on customer satisfaction based on big data analytics. By applying the suggested methodology, marketing managers can gain in-depth insights into consumer needs and reflect this knowledge in their future product or service improvement.

中文翻译：

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特征级情感对产品评级的不对称影响：二元自然语言处理（NLP）分析的应用

目的

在线消费者评论 (OCR) 中反映的感知属性性能评估对于及时获得营销见解至关重要。本研究提出了一种文本挖掘方法来衡量特征级别的消费者情绪及其对整体产品评级的不对称影响。

设计/方法/方法

这项研究使用了为 Amazon.com 上的 14 款无线耳塞产品生成的 49,130​​ 个 OCR。使用二元自然语言处理 (NLP) 分析识别主要质量维度和相关情感词的词组合。这项研究结合了情感词典和与特征相关的二元组，并在评论中测量了特征级别的情感分数。此外，作者还研究了特征级别情绪对产品评级的影响。

发现

结果表明，从文本评论中衡量的产品功能的客户情绪显着且不对称地影响整体评级。基于客户满意度的三因素理论，无线耳塞的关键质量维度分为基本因素、兴奋因素和性能因素。

原创性/价值

本研究提供了一种基于大数据分析来评估产品的客户特征级别评估及其对客户满意度的影响的新方法。通过应用建议的方法，营销经理可以深入了解消费者的需求，并将这些知识反映在他们未来的产品或服务改进中。