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Social Media platforms (e.g., Twitter, Facebook, etc.) are used heavily by public to provide news, opinions, and reactions towards events or topics. Integrating such data with the event or topic factual data could provide a more comprehensive understanding of the underlying event or topic. Targeting this, we present our visual analytics tool, called VC-FaT, that integrates peoples' tweet data regarding crimes in San Francisco city with the city factual crime data. VC-FaT provides a number of interactive visualizations using both data sources for better understanding and exploration of crime activities happened in the city during a period of five years., Comment: 2 pages

Twitter is one of the popular social media platforms where people share news or reactions towards an event or topic using short text messages called "tweets". Emotion analysis in these tweets can play a vital role in understanding peoples' feelings towards the underlying event or topic. In this work, we present our visual analytics tool, called TECVis, that focuses on providing comparison views of peoples' emotion feelings in tweets towards an event or topic. The comparison is done based on geolocations or timestamps. TECVis provides several interaction and filtering options for navigation and better exploration of underlying tweet data for emotion feelings comparison., Comment: 2 pages

Visual exploration of multi-classification models with large number of classes would help machine learning experts in identifying the root cause of a problem that occurs during learning phase such as miss-classification of instances. Most of the previous visual analytics solutions targeted only a few classes. In this paper, we present our interactive visual analytics tool, called MultiCaM-Vis, that provides \Emph{overview+detail} style parallel coordinate views and a Chord diagram for exploration and inspection of class-level miss-classification of instances. We also present results of a preliminary user study with 12 participants.

The recent advancements in machine learning have motivated researchers to generate classification models dealing with hundreds of classes such as in the case of image datasets. However, visualization of classification models with high number of classes and inter-model comparison in such classification problems are two areas that have not received much attention in the literature, despite the ever-increasing use of classification models to address problems with very large class categories. In this paper, we present our interactive visual analytics tool, called Circles, that allows a visual inter-model comparison of numerous classification models with 1K classes in one view. To mitigate the tricky issue of visual clutter, we chose concentric a radial line layout for our inter-model comparison task. Our prototype shows the results of 9 models with 1K classes