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Most existing unsupervised approaches to detect topic sentiment in social texts consider only the text sequences in corpus and put aside social dynamics, as leads to algorithm’s disability to discover true sentiment of social users. To address the issue, a probabilistic graphical model LDTSM (Long-term Dependence Topic-Sentiment Mixture) is proposed, which introduces dependency distance and uses the dynamics of social media to achieve the perfect combination of inheriting historical topic sentiment and fitting topic sentiment distribution underlying in current social texts. Extensive experiments on real-world SinaWeibo datasets show that LDTSM significantly outperforms JST, TUS-LDA and dNJST in terms of sentiment classification accuracy, with better inference convergence, and topic and sentiment evolution analysis results demonstrate that our approach is promising.

Accurate and real-time perception of the operating status of rolling bearings, which constitute a key component of rotating machinery, is of vital significance. However, most existing solutions not only require substantial expertise to conduct feature engineering, but also seldom consider the temporal correlation of sensor sequences, ultimately leading to complex modeling processes. Therefore, we present a novel model, named Attention-based Equitable Segmentation Gated Recurrent Unit Networks (AESGRU), to improve diagnostic accuracy and model-building efficiency. Specifically, our proposed AESGRU consists of two modules, an equitable segmentation approach and an improved deep model. We first transform the original dataset into time-series segments with temporal correlation, so that the model enables end-to-end learning from the strongly correlated data. Then, we deploy a single-layer bidirectional GRU network, which is enhanced by attention mechanism, to capture the long-term dependency of sensor segments and focus limited attention resources on those informative sampling points. Finally, our experimental results show that the proposed approach outperforms previous approaches in terms of the accuracy.

Recognition of 3D shapes is a fundamental task in computer vision. In recent years, view-based deep learning has emerged as an effective approach for 3D shape recognition. Most existing view-based methods treat the views of an object as an unordered set, which ignores the dynamic relations among the views, e.g. sequential semantic dependencies. In this paper, modeling the views of an object by a sequence, we aim at exploiting the long-term dependencies among different views for shape recognition, which is done by constructing a sequence-aware view aggregation module based on the bi-directional Long Short-Term Memory network. It is shown that our view aggregation module not only captures the bi-directional dependencies in view sequences, but also enjoys the robustness to circular shifts of input sequences. Incorporating the aggregation module into a standard convolutional network architecture, we develop an effective method for 3D shape classification and retrieval. Our method was evaluated on the ModelNet40/10 and ShapeNetCore55 datasets. The results show the encouraging performance gain from exploiting long-term dependencies in view sequences, as well as the superior performance of our method compared to the existing ones.