Integrating Sentiment Analysis and Term Associations with Geo-Temporal Visualizations on Customer Feedback Streams

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 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)