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Tools for Qualitative Analysis of Online Communication Data Project Overview: Researchers working with social media and online communication data often apply mixed methods, including both quantitative and qualitative analysis. While statistics and computational modeling can reveal general scientifc over large datasets, qualitative analysis can generate rich descriptions and theory.

By combining both approaches, researchers get the strengths of Singles sex Barwon Heads. However, qualitative analysis, specifically coding, requires manual human interpretation and is quite labor-intensive. Visual analytics can support this process and facilitate richer insights.

Aeonium — Visual Analytics for Qualitative Coding: Qualitative coding is often used by social scientists to explore and analyze their datasets. scienific

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As the scale of the data grows, coding the whole dataset may not be feasible. This project focuses on parsing out de requirements to facilitate the coding process of social scientists, and to leverage machine learning and visualization to highlight subsets of data that are ambiguous or inconsistent between coders. Text Prizm — Collaborative Coding for Chat Data: Qualitative coding is laborious, and online social chat and communication datasets are large.

Spreheets are commonly used for coding this type of data, but they support the task awkwardly at best. As part of our research analyzing the role of emotion in a scientific scientific chat room, we deed and developed Text Prizm, a web application to help analyze social media Friday Harbor girls that fuck Friday Harbor seeking Leighton Buzzard fit daddy online communication content such as chat logs or Twitter data.

Text Prizm provides simultaneous collaborative coding with an efficient keyboard interaction. ALOE — Classifying Emotion in Text-based Chat: Machine learning technology has potential to support qualitative analysis by learning models from smaller manually-coded datasets. These models can then be applied to analyze much larger amounts of data.

We developed the machine learning tool ALOE to train and test machine learning classifiers for automatically labeling chat messages with different emotion or affect. The software takes as input a CSV file containing timestamped scuentific messages with labels for training and produces a trained Support Vector Machine classifier. PDF Chen, N.

Brooks, M. Statistical affect detection in collaborative chat. Adapting grounded theory to construct a taxonomy of affect in collaborative online chat.

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