Home // HUSO 2016, The Second International Conference on Human and Social Analytics // View article
Authors:
Veronique Hoste
Cynthia Van Hee
Karolien Poels
Keywords: emotion detection; social media; natural language processing; organizational crisis; crisis communication
Abstract:
Social media, and in particular Twitter, are increasingly being utilized during crises. It has been shown that tweets offer valuable real-time information for decision-making. Given the vast amount of data available on the Web, there's a need for intelligent ways to select and retrieve the desired information. Analyzing sentiment and emotions in online text is one option for distinguishing relevant from irrelevant information. In this study, we investigate to what extent automatic sentiment analysis techniques can be used for detecting crisis emotions on Twitter. Therefore, a corpus of tweets posted after the crash of Germanwings Flight 9525 was built and labeled with polarity and emotion information. Preliminary results show better classification results for the negative sentiment class compared to the positive class. An analysis of the more fine-grained emotion classification reveals that sympathy and anger are the most frequently expressed emotions in our corpus. To further enhance the performance of emotion classification in online crisis communication, it is crucial to accurately detect i) the object of the crisis emotion and ii) the characteristics of the sender.
Pages: 29 to 32
Copyright: Copyright (c) IARIA, 2016
Publication date: November 13, 2016
Published in: conference
ISSN: 2519-8351
ISBN: 978-1-61208-519-7
Location: Barcelona, Spain
Dates: from November 13, 2016 to November 17, 2016