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Yoon-Sik Cho

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3 papers

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AAAI Conference 2025 Conference Paper

Beyond Single Emotion: Multi-label Approach to Conversational Emotion Recognition

Yujin Kang
Yoon-Sik Cho

Emotion recognition in conversation (ERC) has been promoted with diverse approaches in the recent years. However, many studies have pointed out that emotion shift and confusing labels make it difficult for models to distinguish between different emotions. Existing ERC models suffer from these problems when the emotions are forced to be mapped into single label. In this paper, we utilize our strategies for extending single label to multi-labels. We then propose a multi-label classification framework for emotion recognition in conversation (ML-ERC). Specifically, we introduce weighted supervised contrastive learning tailored for multi-label, which can easily applied to previous ERC models. The empirical results on existing task with single label support the efficacy of our approach, which is more effective in the most challenging settings: emotion shift or confusing labels. We also evaluate ML-ERC with the multi-labels we produced to support our contrastive learning scheme.

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AAAI Conference 2014 Conference Paper

Where and Why Users “Check In”

Yoon-Sik Cho
Greg Ver Steeg
Aram Galstyan

The emergence of location based social network (LBSN) services makes it possible to study individuals’ mobility patterns at a fine-grained level and to see how they are impacted by social factors. In this study we analyze the check-in patterns in LBSN and observe significant temporal clustering of check-in activities. We explore how self-reinforcing behaviors, social factors, and exogenous effects contribute to this clustering and introduce a framework to distinguish these effects at the level of individual check-ins for both users and venues. Using check-in data from three major cities, we show not only that our model can improve prediction of future check-ins, but also that disentangling of different factors allows us to infer meaningful properties of different venues.

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AAAI Conference 2011 Conference Paper

Co-Evolution of Selection and Influence in Social Networks

Yoon-Sik Cho
Greg Steeg
Aram Galstyan

Many networks are complex dynamical systems, where both attributes of nodes and topology of the network (link structure) can change with time. We propose a model of co-evolving networks where both node attributes and network structure evolve under mutual in- ﬂuence. Speciﬁcally, we consider a mixed membership stochastic blockmodel, where the probability of observing a link between two nodes depends on their current membership vectors, while those membership vectors themselves evolve in the presence of a link between the nodes. Thus, the network is shaped by the interaction of stochastic processes describing the nodes, while the processes themselves are inﬂuenced by the changing network structure. We derive an efﬁcient variational inference procedure for our model, and validate the model on both synthetic and real–world data.

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