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Twitter currently receives about 190 million tweets (small text-based Web posts) a day, in which people share their comments regarding a wide range of topics. A large number of tweets include opinions about products and services. However, with Twitter being a relatively new phenomenon, these tweets are underutilized as a source for evaluating customer(More)
Today, online stores collect a lot of customer feedback in the form of surveys, reviews, and comments. This feedback is categorized and in some cases responded to, but in general it is underutilized – even though customer satisfaction is essential to the success of their business. In this paper, we introduce several new techniques to interactively analyze(More)
This article describes automatic methods and interactive visualizations that are tightly coupled with the goal to enable users to detect interesting portions of text document streams. In this scenario the interestingness is derived from the sentiment, temporal density, and context coherence that comments about features for different targets (e.g., persons,(More)
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(More)
Figure 1: A pipeline of integration sentiment analysis and term associations with geo-temporal visualizations for effective visualization of large customer feedback streams from Twitter ABSTRACT 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(More)
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