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Automatic emotion detection in text streams by analyzing Twitter data

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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.

Keywords

Supervised emotion learning Real-time emotion detection Twitter events analysis Public emotion sensing Text stream classification Soft classification 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Maryam Hasan
    • 1
  • Elke Rundensteiner
    • 1
  • Emmanuel Agu
    • 1
  1. 1.Computer Science DepartmentWorcester Polytechnic InstituteWorcesterUSA

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