IVML  
  about | r&d | publications | courses | people | links
   

G. Drakopoulos, A. Kanavos, Ph. Mylonas, S. Sioutas
Discovering Sentiment Polarity Alternations in Twitter Conversations with Hilbert- Huang Spectrum
Evolving Systems, Springer, July 2020
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. 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.
07 July , 2020
G. Drakopoulos, A. Kanavos, Ph. Mylonas, S. Sioutas, "Discovering Sentiment Polarity Alternations in Twitter Conversations with Hilbert- Huang Spectrum", Evolving Systems, Springer, July 2020
[ save PDF] [ BibTex] [ Print] [ Back]

© 00 The Image, Video and Multimedia Systems Laboratory - v1.12