Social Emotion Mining Techniques for Facebook Posts Reaction Prediction

As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called `reactions'. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages of firms/companies (like supermarket chains). For this purpose, we collected posts (and their reactions) from Facebook pages of large supermarket chains and constructed a dataset which is available for other researches. In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. Results of the neural networks were improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post. The final model (a combination of neural network and a baseline emotion miner) is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.135.Comment: 10 pages, 13 figures and accepted at ICAART 2018. (Dataset: https://github.com/jerryspan/FacebookR

Why does higher working memory capacity help you learn?

Algorithms for approximate Bayesian inference, such as Monte Carlo methods, provide one source of models of how people may deal with uncertainty in spite of limited cognitive resources. Here, we model learning as a process of sequential sampling, or ‘particle filtering’, and suggest that an individual’s working memory capacity (WMC) may be usefully modelled in terms of the number of samples, or ‘particles’, that are available for inference. The model qualitatively captures two distinct effects reported recently, namely that individuals with higher WMC are better able to (i) learn novel categories, and (ii) flexibly switch between different categorization strategie

Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model

Multiple object tracking is a task commonly used to investigate the architecture of human visual attention. Human participants show a distinctive pattern of successes and failures in tracking experiments that is often attributed to limits on an object system, a tracking module, or other specialized cognitive structures. Here we use a computational analysis of the task of object tracking to ask which human failures arise from cognitive limitations and which are consequences of inevitable perceptual uncertainty in the tracking task. We find that many human performance phenomena, measured through novel behavioral experiments, are naturally produced by the operation of our ideal observer model (a Rao-Blackwelized particle filter). The tradeoff between the speed and number of objects being tracked, however, can only arise from the allocation of a flexible cognitive resource, which can be formalized as either memory or attention

Individual mentalizing ability boosts flexibility toward a linguistic marker of social distance: An ERP investigation

Sentence-final particles (SFPs) as bound morphemes in Japanese have no obvious effect on the truth conditions of a sentence. However, they encompass a diverse range of usages, from typical to atypical, according to the context and the interpersonal relationships in the specific situation. The most frequent particle,-ne, is typically used after addressee-oriented propositions for information sharing, while another frequent particle,-yo, is typically used after addresser-oriented propositions to elicit a sense of strength. This study sheds light on individual differences among native speakers in flexibly understanding such linguistic markers based on their mentalizing ability (i.e., the ability to infer the mental states of others). Two experiments employing electroencephalography (EEG) consistently showed enhanced early posterior negativities (EPN) for atypical SFP usage compared to typical usage, especially when understanding-ne compared to -yo, in both an SFP appropriateness judgment task and a content comprehension task. Importantly, the amplitude of the EPN for atypical usages of-ne was significantly higher in participants with lower mentalizing ability than in those with a higher mentalizing ability. This effect plausibly reflects low-ability mentalizers' stronger sense of strangeness toward atypical-ne usage. While high-ability mentalizers may aptly perceive others' attitudes via their various usages of-ne, low-ability mentalizers seem to adopt a more stereotypical understanding. These results attest to the greater degree of difficulty low-ability mentalizers have in establishing a smooth regulation of interpersonal distance during social encounters