M. Hao, Christian Rohrdantz, H. Janetzko, D. Keim, U. Dayal, L. Haug, M. Hsu
{"title":"Integrating sentiment analysis and term associations with geo-temporal visualizations on customer feedback streams","authors":"M. Hao, Christian Rohrdantz, H. Janetzko, D. Keim, U. Dayal, L. Haug, M. Hsu","doi":"10.1117/12.912202","DOIUrl":null,"url":null,"abstract":"Twitter currently receives over 190 million tweets (small text-based Web posts) and manufacturing companies receive over 10 \nthousand web product surveys a day, in which people share their thoughts regarding a wide range of products and their features. A \nlarge number of tweets and customer surveys include opinions about products and services. However, with Twitter being a relatively \nnew phenomenon, these tweets are underutilized as a source for determining customer sentiments. To explore high-volume customer \nfeedback streams, we integrate three time series-based visual analysis techniques: (1) feature-based sentiment analysis that extracts, \nmeasures, and maps customer feedback; (2) a novel idea of term associations that identify attributes, verbs, and adjectives frequently \noccurring together; and (3) new pixel cell-based sentiment calendars, geo-temporal map visualizations and self-organizing maps to \nidentify co-occurring and influential opinions. We have combined these techniques into a well-fitted solution for an effective analysis \nof large customer feedback streams such as for movie reviews (e.g., Kung-Fu Panda) or web surveys (buyers).","PeriodicalId":89305,"journal":{"name":"Visualization and data analysis","volume":"37 1","pages":"82940H"},"PeriodicalIF":0.0000,"publicationDate":"2012-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Visualization and data analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.912202","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
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 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).