10,082 research outputs found
Scalable Privacy-Compliant Virality Prediction on Twitter
The digital town hall of Twitter becomes a preferred medium of communication
for individuals and organizations across the globe. Some of them reach
audiences of millions, while others struggle to get noticed. Given the impact
of social media, the question remains more relevant than ever: how to model the
dynamics of attention in Twitter. Researchers around the world turn to machine
learning to predict the most influential tweets and authors, navigating the
volume, velocity, and variety of social big data, with many compromises. In
this paper, we revisit content popularity prediction on Twitter. We argue that
strict alignment of data acquisition, storage and analysis algorithms is
necessary to avoid the common trade-offs between scalability, accuracy and
privacy compliance. We propose a new framework for the rapid acquisition of
large-scale datasets, high accuracy supervisory signal and multilanguage
sentiment prediction while respecting every privacy request applicable. We then
apply a novel gradient boosting framework to achieve state-of-the-art results
in virality ranking, already before including tweet's visual or propagation
features. Our Gradient Boosted Regression Tree is the first to offer
explainable, strong ranking performance on benchmark datasets. Since the
analysis focused on features available early, the model is immediately
applicable to incoming tweets in 18 languages.Comment: AffCon@AAAI-19 Best Paper Award; Presented at AAAI-19 W1: Affective
Content Analysi
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
Bivariate Beta-LSTM
Long Short-Term Memory (LSTM) infers the long term dependency through a cell
state maintained by the input and the forget gate structures, which models a
gate output as a value in [0,1] through a sigmoid function. However, due to the
graduality of the sigmoid function, the sigmoid gate is not flexible in
representing multi-modality or skewness. Besides, the previous models lack
modeling on the correlation between the gates, which would be a new method to
adopt inductive bias for a relationship between previous and current input.
This paper proposes a new gate structure with the bivariate Beta distribution.
The proposed gate structure enables probabilistic modeling on the gates within
the LSTM cell so that the modelers can customize the cell state flow with
priors and distributions. Moreover, we theoretically show the higher upper
bound of the gradient compared to the sigmoid function, and we empirically
observed that the bivariate Beta distribution gate structure provides higher
gradient values in training. We demonstrate the effectiveness of bivariate Beta
gate structure on the sentence classification, image classification, polyphonic
music modeling, and image caption generation.Comment: AAAI 202
- …