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Discovering sentiment potential in Twitter conversations with Hilbert–Huang spectrum

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Abstract

Does a tweet with specific emotional content posted by an influential account have the capability to shape or even completely alter the opinions of its readers? Moreover, can other influential accounts further enhance its original emotional potential by retweeting it and, thus, letting their followers read it? Real Twitter conversations seem to imply an affirmative answer to both questions. If this is indeed the case, then what is the key for not only successfully reaching to a large number of accounts but also for convincingly offering an alternative perspective via affective means, therefore triggering a large scale opinion change in an ongoing Twitter conversation? This work primarily focuses on determining which tweets cause multiple sentiment polarity alternations to occur based on a window segmentation approach. Moreover, an offline framework for discovering affective pivot points in a conversation based on its Hilbert–Huang spectrum, which has close ties to the Fourier transform, is introduced. Finally, given that it is highly desirable to track the sentiment shifts of a Twitter conversation while it unfolds, an adaptive scheme is presented for approximating the window sizes obtained by the offline methodology. As a concrete example, the abovementioned methodologies are applied to three recent long Twitter discussions and the results are analyzed.

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Notes

  1. http://sentistrength.wlv.ac.uk/.

  2. http://twitter4j.org/en/index.html.

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Acknowledgements

This article is part of project Tensor 451, a long term research initiative whose primary objective is the development of novel, scalable, numerically stable, and interpretable tensor and higher order analytics.

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Authors and Affiliations

  1. Department of Informatics, Ionian University, Corfu, Greece

    Georgios Drakopoulos, Phivos Mylonas & Spyros Sioutas

  2. Computer Engineering and Informatics Department, University of Patras, Patras, Greece

    Andreas Kanavos & Spyros Sioutas

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  1. Georgios Drakopoulos
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  2. Andreas Kanavos
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Correspondence to Andreas Kanavos.

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Drakopoulos, G., Kanavos, A., Mylonas, P. et al. Discovering sentiment potential in Twitter conversations with Hilbert–Huang spectrum. Evolving Systems 12, 3–17 (2021). https://doi.org/10.1007/s12530-020-09348-z

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