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首页> 外文期刊>Journal of Business Research >Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning
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Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning

机译：在线临界审查分类在响应策略和服务提供商评级：从启发式处理，深度学习的情感分析的算法

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摘要

This research proposes and tests mechanisms for defining and identifying the critical online consumer reviews that firms could prioritize to optimize their online response strategies, while incorporating the latest artificial intelligence (AI) technology to deal with the overwhelming volume of information. Three sets of analytical tools are introduced: Heuristic Processing, Linguistic Feature Analysis, and Deep Learning-based Natural Language Processing (NLP), to extract review information. Twelve algorithms to classify critical reviews were developed accordingly and empirically tested for their effectiveness. Our econometric analysis of 110,146 online reviews from a chain operation in hospitality industry over seven years identifies six outstanding algorithms. Firm value rating, comment length, valence, and certain consumer emotions, in addition to past comment-response behavior, are found to be superior in predicting incoming review criticality. However, the service attributes such as urgency to reply and the feasibility of actions to take are not as informative.
机译：本研究提出并测试了定义和识别企业可以优先考虑优化其在线响应策略的关键在线消费者评论的机制，同时纳入最新的人工智能（AI）技术来处理压倒性的信息量。介绍了三套分析工具：启发式处理，语言特征分析和基于深度学习的自然语言处理（NLP），提取审查信息。十二次算法来分类关键评论，并在经验上进行了效果。我们的经济学分析来自Hospitiality的连锁业务的110,146在线评论超过七年，确定了六种优秀的算法。除了过去的评论 - 响应行为之外，还有公司价值评级，评论长，价值和某些消费者情绪，发现在预测进入审查的关键性方面是优越的。但是，诸如回复的紧迫性等服务属性以及采取的行动的可行性并不是任何信息。

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