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Exploring the drivers of customers' brand attitudes of online travel agency services: A text-mining based approach

机译：探索客户品牌在线旅行社服务的品牌态度的驱动程序：基于文本挖掘的方法

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This paper aims to explore the important qualitative aspects of online user-generated-content that reflects customers' brand-attitudes. Additionally, the qualitative aspects can help service-providers understand customers' brand-attitudes by focusing on the important aspects rather than reading the entire review, which will save both their time and effort. We have utilised a total of 10,000 reviews from TripAdvisor (an online-travel-agency provider). This study has analysed the data using statistical-technique (logistic regression), predictive-model (artificial-neural-networks) and structural-modelling technique to understand the most important aspects (i.e. sentiment, emotion or parts-of-speech) that can help to predict customers' brand-attitudes. Results show that sentiment is the most important aspect in predicting brand-attitudes. While total sentiment content and content polarity have significant positive association, negative high-arousal emotions and low-arousal emotions have significant negative association with customers' brand attitudes. However, parts-of-speech aspects have no significant impact on brand attitude. The paper concludes with implications, limitations and future research directions.
机译：本文旨在探讨在线用户生成的内容的重要定性方面，反映客户品牌态度。此外，定性方面可以通过专注于重要方面来帮助服务提供者了解客户的品牌态度，而不是阅读整个审查，这将挽救他们的时间和努力。我们从TripAdvisor（一家在线旅行社提供商）共使用10,000条点评。本研究已经使用统计技术（Logistic回归），预测模型（人工神经网络）和结构建模技术分析了数据，以了解可以的最重要方面（即情绪，情感或言论）帮助预测客户的品牌态度。结果表明，情绪是预测品牌态度中最重要的方面。虽然总情感含量和内容极性具有显着的积极关联，但负面高唤醒情绪和低唤醒情绪与客户品牌态度具有显着的负面关联。然而，致辞方面对品牌态度没有显着影响。本文以影响，限制和未来的研究方向得出结论。

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