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Visual sentiment analysis of customer feedback streams using geo-temporal term associations

机译：使用地理时间术语关联对客户反馈流进行视觉情感分析

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摘要

Large manufacturing companies frequently receive thousands of web surveys every day. People share their thoughts regarding a wide range of products, their features, and the service they received. In addition, more than 190 million tweets (small text Web posts) are generated daily. Both survey feedback and tweets are underutilized as a source for understanding customer sentiments. To explore high-volume customer feedback streams, in this article, we introduce four time series visual analysis techniques: (1) feature-based sentiment analysis that extracts, measures, and maps customer feedback; (2) a novel way of determining term associations that identify attributes, verbs, and adjectives frequently occurring together; (3) a self-organizing term association map and a pixel cell-based sentiment calendar to identify co-occurring and influential opinion; and (4) a new geo-based term association technique providing a key term geo map to enable the user to inspect the statistical significance and the sentiment distribution of individual key terms. We have used and evaluated these techniques and combined them into a well-fitted solution for an effective analysis of large customer feedback streams such as web surveys (from product buyers] and Twitter (e.g. from Kung-Fu Panda movie reviewers).
机译：大型制造公司每天经常收到数千个网络调查。人们分享他们对各种各样的产品，功能和所获得的服务的想法。此外，每天产生超过1.9亿条推文（小型文本Web帖子）。调查反馈和推文都未充分利用来理解客户情绪。为了探索大量的客户反馈流，在本文中，我们介绍了四种时间序列可视化分析技术：（1）基于特征的情感分析，用于提取，度量和映射客户反馈； （2）一种新颖的确定术语关联的方式，这种关联可以识别经常一起出现的属性，动词和形容词； （3）自组织术语关联图和基于像素单元的情感日历，以识别共生和有影响力的观点； （4）一种新的基于地理的术语关联技术，可提供关键术语地理地图，使用户能够检查各个关键术语的统计意义和情感分布。我们已经使用并评估了这些技术，并将它们组合到一个合适的解决方案中，以有效分析大型客户反馈流，例如网络调查（来自产品购买者）和Twitter（例如，来自功夫熊猫电影评论者）。

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