SAEP: A Surrounding-Aware Individual Emotion Prediction Model Combined with T-LSTM and Memory Attention Mechanism

Abstract

The future emotion prediction of users on social media has been attracting increasing attention from academics. Previous studies on predicting future emotion have focused on the characteristics of individuals’ emotion changes; however, the role of the individual’s neighbors has not yet been thoroughly researched. To fill this gap, a surrounding-aware individual emotion prediction model (SAEP) based on a deep encoder–decoder architecture is proposed to predict individuals’ future emotions. In particular, two memory-based attention networks are constructed: The time-evolving attention network and the surrounding attention network to extract the features of the emotional changes of users and neighbors, respectively. Then, these features are incorporated into the emotion prediction task. In addition, a novel variant LSTM is introduced as the encoder of the proposed model, which can effectively extract complex patterns of users’ emotional changes from irregular time series. Extensive experimental results show that the proposed approach outperforms five alternative methods. The SAEP approach has improved by approximately 4.21–14.84% micro F1 on a dataset built from Twitter and 7.30–13.41% on a dataset built from Microblog. Further analyses validate the effectiveness of the proposed time-evolving context and surrounding context, as well as the factors that may affect the prediction results.

Ultra-Short-Term Load Forecasting for Customer-Level Integrated Energy Systems Based on Composite VTDS Models

“…The T-LSTM model [19,20] leverages the interval time between data points as additional features., In Figure 6, the green box represents the network, and the yellow circles represent point-wise operators., t x , , represents, the, current, input , The T-LSTM model [19,20] leverages the interval time between data points as additional features. In Figure 6, the green box represents the network, and the yellow circles represent point-wise operators.…”

illustration of attention mechanism in LSTM algorithm for analyzing its performance

“…and Chung, K [18] proposed the accident risk prediction model based on attention-mechanism LSTM using modality convergence in multi-modal. Wang, Yakun et al [19] developed Surrounding-Aware individual Emotion Prediction model (SAEP) based on a deep encoder & decoder architecture to predict individuals future emotions. Yifeng Zhao and Deyun Chen [20] proposed the bidirectional LSTM and attention mechanism based on the expression EEG multimodal emotion recognition method.…”