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«Keyword-based searching and clustering of news articles have been widely used for news analysis. However, news articles usually have other attributes ...»

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After they finished all three tasks, we also asked the users about their general feeling about our design. The biggest concern we had before conducting the user study was the visual clutter and distraction in the interface. However, the responses to our system from users are quite enthusiastic. Overall, they feel the system is informative, intuitive, and visually appealing. In particular, they think that the visual interface is well organized, and each of the visual component has a very clear purpose, which makes the exploration easy.

We also asked their opinions for each visual component. Most of them appreciate the familiar metaphors, such as wheels, belts, and chains, to intuitively represent document streams and sophisticated relations involving documents, keywords, and sentiment values. Among all the visual elements, the users appreciate the wheel most, because it combines multiple aspects of information in a natural and intuitive way and they do not need to think too much to understand the meanings during the fast exploration. In addition to the wheel, they also agree that the significance curve is a very important feature, because without the overview, they will have no choice but to wildly explore the whole document stream, which could make them feel insecure. For example, during the free exploration, they were all interested in those time points when the curve is very steep, and spent some time there trying to figure out why. Therefore, they also feel that the accuracy of the curve is extremely important. Otherwise, the curve is misleading.

In addition to those positive comments, they also raised a few constructive suggestions. For example, five users feel that using thickness to perform quantitative comparison is not very efficient. Therefore, they suggested we provide an option to show actual numbers when the thicknesses are too similar to compare. Another suggestion is regarding the system control. For example, some of the users would like to control the sensitivity of the wheel to the summarized keyword sentiments, so they can ignore those minor fluctuations and focus more on big patterns. Some of them would like to be able to add or remove keywords during exploration other than choosing them before exploring. We think these suggestions are all very valuable, and intended to explore them in future work. Furthermore, we also plan to deploy to a news Web site to reach more audiences and further improve our system based on their comments.

9. DISCUSSIONS From the experiments, we can see that our system has advantages and can encode a lot of information into one display for analysis such that some unexpected correlations may emerge. Meanwhile, we also identify some weaknesses of our system. Our system can handle thousands of documents and tens of keywords effectively. If the news

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streams contain too many articles, exploration may still take a very long time and reduce its effectiveness. Too many keywords may overwhelm the keyword wheels and cause clutter in the display. To deal with this problem, it is better to use our system together with some data mining techniques to first narrow down the document scope.

Some well-established techniques in the visualization field, such as clutter reduction methods, can also be applied. As our system provides useful information into one display, it is possible that users are overwhelmed and lose their focus. Thus, we recommend that users turn off some features of the system and only focus on one feature at the beginning. After getting familiar with the data, more details can be brought into the display.

10. CONCLUSION In this article, we have presented a visual analytics system for large-scale news streams. Our system aims at providing the multiple attributes of news articles and keywords, the dynamic relations between news articles and keywords, the micro relation among keywords, and the macro relation among documents simultaneously to users for analysis. We designed an original TextWheel which consists of a document transportation belt, one or multiple keyword wheels, and a chain system to connect the belt and wheel. Our system is based on some everyday objects which users are familiar with and thus the learning curve for our system should be low. We demonstrated the effectiveness of our system by applying it to several news corpora related to some major companies, obtaining some interesting findings. The application of our system is not limited to news streams. It can be used to analyze other data in text format (e.g., emails, blogs, and internal memos), and to reveal the macro/micro relations existing in other data formats, such as video clips.

In the future, we plan to further extend our system to encode more attributes of text documents. We believe the integration of our system with other data mining methods will make it more powerful. We also want to encode the uncertainty associated with the sentiment and co-occurrence computations. Although we conducted a user study which has suggested that our subjects agree that our design and system can help them with pattern huntings in macro/micro relations, it is still preliminary and informal.

Therefore, we also plan to conduct a more thorough user study, which involves more subjects, comparison with other tools, and a formal questionnaire-based survey.

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Watch the Story Unfold with TextWheel: Visualization of Large-Scale News Streams 20:17 GRANITZER, M., KIENREICH, W., SABOL, V., ANDREWS, K., AND KLIEBER, W. 2004. Evaluating a system for interactive exploration of large, hierarchically structured document repositories. In Proceedings of the IEEE Symposium on Information Visualization. 127–134.

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LLOYD, L., KECHAGIAS, D., AND SKIENA, S. 2005. Lydia: A system for large-scale news analysis. In Proceedings of the 12th Symposium of String Processing and Information Retrieval (SPIRE’05). Lecture Notes in Computer Science, vol. 3772. Springer, 161–166.

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Received July 2010; revised October 2010; accepted October 2010

ACM Transactions on Intelligent Systems and Technology, Vol. 3, No. 2, Article 20, Publication date: February 2012.

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