Visual Text Analytics for Asynchronous Online Conversations

Abstract

In the last decade, there has been an exponential growth of online conversations thanks to the rise of social media. Analyzing and gaining insights from such conversations can be quite challenging for a user, especially when the discussions become very long. During my doctoral research, I aim to investigate how to integrate Information Visualization with Natural Language Processing techniques to better support the user's task of exploring and analyzing conversations. For this purpose, I consider the following approaches: apply design study methodology in InfoVis to uncover data and task abstractions; apply NLP methods for extracting the identified data to support those tasks; and incorporate human feedback in the text analysis process when the extracted data is noisy and/or may not match the user's mental model, and current tasks. Through a set of design studies, I aim to evaluate the effectiveness of our approaches.