Konversatorium on Friday, November 8, 2019 - 10:30
Visual Active Learning for News Stream Classification (DAEV)
Keeping up with continuous text streams, like daily news, costs a considerable amount of time. We developed an interactive classification interface for text streams that learns user-specific topics from the user's labels and partitions incoming data into these topics.Current approaches that categorize unstructured text documents use pre-trained learning models for text classification. In the case of a continuous text stream, the usefulness is limited, as these models cannot adapt their categories or learn new terminology.
To adapt to changing terminology and to learn user-specific topics, we utilize a variant of active learning in an iterative process of model training.We present visual active learning for text streams by visualizing the topic affiliations in a Star Coordinates visualization. This visualization provides novel direct interaction tools for iterative model training.
We developed a simulation to compare the accuracy of visual active learning and classic active learning.In a preliminary user study, we compared our visualization to a list-based interface for news retrieval and active learning. Through our evaluation, we could show that our visualization is a very effective user interface for active learning of streaming data.
A Visual Exploration Tool for Temporal Analysis of Customer Reviews (DAAV)
Noticing trends in customer review changes over time is a difficult task. Finding a visualisation method that is able to convey large amount of information and aid people to conclusions about content changes over time, is the goal of the thesis. 2D visualisation techniques are used to present content changes in real-life restaurant customer reviews. The developed prototype tool and its techniques are evaluated by test users, who try to gain insights on an example data-set, aiming to distinguish trends, reach conclusions and get proper understanding of the market. The evaluation results are studied and compared in order to determine the tool’s practical usefulness and future improvements.