将情感分析和术语关联与客户反馈流的地理时间可视化相结合

M. Hao, Christian Rohrdantz, H. Janetzko, D. Keim, U. Dayal, L. Haug, M. Hsu
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引用次数: 4

摘要

Twitter目前收到超过1.9亿条tweet(基于文本的小型Web帖子),制造公司每天收到超过1万份网络产品调查,人们在其中分享他们对各种产品及其功能的看法。大量的推文和客户调查包括对产品和服务的意见。然而,由于Twitter是一个相对较新的现象,这些tweet未被充分利用作为确定客户情绪的来源。为了探索大量的客户反馈流,我们整合了三种基于时间序列的可视化分析技术:(1)基于特征的情感分析,提取、测量和映射客户反馈;(2)术语关联的新概念,识别经常一起出现的属性、动词和形容词;(3)新的基于像素单元的情感日历,地理时间地图可视化和自组织地图,以识别共同发生和有影响力的意见。我们已经将这些技术结合到一个合适的解决方案中,用于有效分析大型客户反馈流,如电影评论(例如,功夫熊猫)或网络调查(买家)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating sentiment analysis and term associations with geo-temporal visualizations on customer feedback streams
Twitter currently receives over 190 million tweets (small text-based Web posts) and manufacturing companies receive over 10 thousand web product surveys a day, in which people share their thoughts regarding a wide range of products and their features. A large number of tweets and customer surveys include opinions about products and services. However, with Twitter being a relatively new phenomenon, these tweets are underutilized as a source for determining customer sentiments. To explore high-volume customer feedback streams, we integrate three time series-based visual analysis techniques: (1) feature-based sentiment analysis that extracts, measures, and maps customer feedback; (2) a novel idea of term associations that identify attributes, verbs, and adjectives frequently occurring together; and (3) new pixel cell-based sentiment calendars, geo-temporal map visualizations and self-organizing maps to identify co-occurring and influential opinions. We have combined these techniques into a well-fitted solution for an effective analysis of large customer feedback streams such as for movie reviews (e.g., Kung-Fu Panda) or web surveys (buyers).
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