Name
Abstract | ||
|---|---|---|
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, institutions, product attributes, topics, etc.) have. Contributions are made at different stages of the visual analytics pipeline, including novel ways to visualize salient temporal accumulations for further exploration. Moreover, based on the visualization, an automatic algorithm aims to detect and preselect interesting time interval patterns for different features in order to guide analysts. The main target group for the suggested methods are business analysts who want to explore time-stamped customer feedback to detect critical issues. Finally, application case studies on two different datasets and scenarios are conducted and an extensive evaluation is provided for the presented intelligent visual interface for feature-based sentiment exploration over time. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2089094.2089102 | ACM TIST |
Keywords | Field | DocType |
preselect interesting time interval,automatic algorithm,intelligent visual interface,different target,text document streams,automatic method,different stage,feature-based visual sentiment analysis,interesting portion,feature-based sentiment exploration,different datasets,different feature,visual analytics,interactive visualization,sentiment analysis,time series,text mining | Data mining,Computer science,Visual analytics,Artificial intelligence,Text mining,Information retrieval,Visualization,Sentiment analysis,Coherence (physics),Feature based,Machine learning,Text document,Salient | Journal |
Volume | Issue | ISSN |
3 | 2 | 2157-6904 |
Citations | PageRank | References |
23 | 1.04 | 18 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Rohrdantz | 1 | 205 | 13.86 |
| Ming C. Hao | 2 | 23 | 1.04 |
| Umeshwar Dayal | 3 | 8452 | 2538.92 |
| Lars-Erik Haug | 4 | 87 | 4.26 |
| Daniel A. Keim | 5 | 7704 | 1141.60 |