With increasingly high volumes of conversations across social media, the rapid detection of emotions is of significant strategic value to industry practitioners. Summarizing large volumes of text with computational linguistics and visual analytics allows for several new possibilities from general trend detection to specific applications in marketing practice, such as monitoring product launches, campaigns and public relations milestones. After collecting 1.6 million user-tagged feelings from 12 million online posts that mention emotions, we utilized machine learning techniques towards building an automatic 'feelings meter'; a tool for both researchers and practitioners to automatically detect emotional dimensions from text. Following several iterations, the test version has now taken shape as emotionVis, a dashboard prototype for inferring emotions from text while presenting the results for visual analysis.
['Christopher J. Zimmerman', 'Mari-Klara Stein', 'Daniel Hardt', 'Christian Danielsen', 'Ravikiran Vatrapu']
emotionVis: Designing an Emotion Text Inference Tool for Visual Analytics