Mining Social Emotions from Affective Text

Bao, Shenghua; Xu, Shun; Zhang, Li; Yan, Rong; Su, Zhong; Han, Dingyi; Yu, Yong

doi:10.1109/tkde.2011.188

Cited by 145 publications

(107 citation statements)

References 23 publications

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“…It is worth noting that even though there have been several studies that introduce latent variables for merging various factors to jointly estimate document contents, which have the obvious differentiation with our RTM. For instance, in topic modeling of document, Rosen-Zvi et al [39] introduced an author latent variable, and Bao et al [40] introduced an emotion latent variable. Both of them first generated the introduced variables (emotion or author) from a specific distribution, then generated a latent topic from a multinomial distribution conditioned on generated variable, and finally generated document terms from another multinomial distribution based on latent topics.…”

Section: Regional Topic Model and Its Online Inference Algorithmmentioning

confidence: 99%

A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining

2018

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Abstract:The events location and real-time computational performance of crowd scenes continuously challenge the field of video mining. In this paper, we address these two problems based on a regional topic model. In the process of video topic modeling, region topic model can simultaneously cluster motion words of video into motion topics, and the locations of motion into motion regions, where each motion topic associates with its region. Meanwhile, a hybrid stochastic variational Gibbs sampling algorithm is developed for inference of our region topic model, which has the ability of inferring in real time with massive video stream dataset. We evaluate our method on simulate and real datasets. The comparison with the Gibbs sampling algorithm shows the superiorities of proposed model and its online inference algorithm in terms of anomaly detection.

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Section: Regional Topic Model and Its Online Inference Algorithmmentioning

confidence: 99%

A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining

2018

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“…Another category is to assign each label a topic distribution instead of a word distribution, such as Author topic Model Rosen-Zvi et al 2010) and Emotion Topic Model (Bao et al 2012). Each label is first associated with a topic distribution, and each topic is further associated with a word distribution.…”

Section: Generative Models For Multi-label Learningmentioning

confidence: 99%

A Bayesian nonparametric model for multi-label learning

Xuan

Zhang

et al. 2017

Mach Learn

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Multi-label learning has become a significant learning paradigm in the past few years due to its broad application scenarios and the everincreasing number of techniques developed by researchers in this area. Among existing state-of-the-art works, generative statistical models are characterized by their good data generalization and robustness on large number of labels through learning a low-dimensional label embedding. However, one issue of this branch of models is that the number of dimensions needs to be fixed in advance, which is difficult and inappropriate in many real-world settings. In this paper, we propose a Bayesian nonparametric model to resolve this issue. More specifically, we extend a Gamma-negative binomial process to three levels in order to capture the label-instance-feature structure. Furthermore, a mixing strategy for Gamma processes is designed to account for the multiple labels of an instance. The mixed process also leads to a difficulty in model inference, so an efficient Gibbs sampling inference algorithm is then developed to resolve this difficulty. Experiments on several real-world datasets show the performance of the proposed model on multi-label learning tasks, comparing with three state-of-the-art models from the literature.

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“…Major work in this area has focussed on the single-label classification techniques where the affective text is classified into one emotion category including SemEval-2007 tasks. Lin et al [9] studied Chinese texts for reader emotion prediction and collected data from Yahoo! and Kimo News to classify text into eight emotion categories.…”

Section: 11mentioning

confidence: 99%

“…Topic here refers to any object, entity or real world event that indicates the subject or context of the sentiment. To solve the problems faced in word-level approach, topic models have been developed like Emotion Topic model (ETM) which introduced an intermediate layer into LDA, in which a topic acts as an important component of an emotion [9]. These were single-label topic models.…”

Section: 22mentioning

confidence: 99%

“…Many online news services now provide a service that lets users convey their emotions after browsing a news article, and then records the emotional responses shared by readers from a set of predefined emotion labels incrementally. The aggregation of such emotional responses is known as social emotions [9]. Mining readers' emotions from textual content is considered to be a predictive mining task through which evoked emotions of readers are foreseen and analyzed.…”

Section: Introductionmentioning

confidence: 99%

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