Name
Abstract | ||
|---|---|---|
The blogosphere allows analysts to track opinions and sentiments of individuals, groups or the general public with large sample sizes regarding many topics. Essential for the sentiment analysis are visualizations. The visual understanding of large corpora's sentiment is far more effective than relying on textual representations of the analyzed content. Users are very interested in changes in the public opinion. Thus, the identification of patterns is of high interest. In this paper, we propose a cluster heat map visualization for sentiment visualization that displays the sentiment development of various related terms over time intervals. As we want to encourage the discovery of patterns over multiple related topics, we apply an ordering algorithm based on dimensionality reduction to the cluster heat map and improve upon the ordering algorithm to enable fast pattern recognition. |
Year | Venue | Field |
|---|---|---|
2015 | PROCEEDINGS OF THE 2015 IEEE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (IEEE DSAA 2015) | Data mining,Hot spot (veterinary medicine),Data visualization,Dimensionality reduction,Information retrieval,Computer science,Sentiment analysis,Visualization,Public opinion,Blogosphere |
DocType | Citations | PageRank |
Conference | 1 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrick Hennig | 1 | 14 | 7.38 |
| Philipp Berger | 2 | 17 | 8.14 |
| Maximilian Brehm | 3 | 1 | 0.34 |
| Bastien Grasnick | 4 | 1 | 0.68 |
| Jonathan Herdt | 5 | 1 | 0.34 |
| Christoph Meinel | 6 | 2341 | 319.90 |