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International Journal of Data Science and Analytics

Erschienen in:

09.02.2018 | Regular Paper

Automatic emotion detection in text streams by analyzing Twitter data

verfasst von: Maryam Hasan, Elke Rundensteiner, Emmanuel Agu

Erschienen in: International Journal of Data Science and Analytics | Ausgabe 1/2019

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Abstract

Techniques to detect the emotions expressed in microblogs and social media posts have a wide range of applications including, detecting psychological disorders such as anxiety or depression in individuals or measuring the public mood of a community. A major challenge for automated emotion detection is that emotions are subjective concepts with fuzzy boundaries and with variations in expression and perception. To address this issue, a dimensional model of affect is utilized to define emotion classes. Further, a soft classification approach is proposed to measure the probability of assigning a message to each emotion class. We develop and evaluate a supervised learning system to automatically classify emotion in text stream messages. Our approach includes two main tasks: an offline training task and an online classification task. The first task creates models to classify emotion in text messages. For the second task, we develop a two-stage framework called EmotexStream to classify live streams of text messages for the real-time emotion tracking. Moreover, we propose an online method to measure public emotion and detect emotion burst moments in live text streams.

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Titel: Automatic emotion detection in text streams by analyzing Twitter data
verfasst von: Maryam Hasan
Elke Rundensteiner
Emmanuel Agu
Publikationsdatum: 09.02.2018
Verlag: Springer International Publishing
Erschienen in: International Journal of Data Science and Analytics / Ausgabe 1/2019
Print ISSN: 2364-415X
Elektronische ISSN: 2364-4168
DOI: https://doi.org/10.1007/s41060-018-0096-z

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