Aspect based sentiment analysis (ABSA) can provide more detailed information than general sentiment analysis, because it aims to predict the sentiment polarities of the given aspects or entities in text. We summarize previous approaches into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). Most previous approaches employ long short-term memory and attention mechanisms to predict the sentiment polarity of the concerned targets, which are often complicated and need more training time. We propose a model based on convolutional neural networks and gating mechanisms, which is more accurate and efficient. First, the novel Gated Tanh-ReLU Units can selectively output the sentiment features according to the given aspect or entity. The architecture is much simpler than attention layer used in the existing models. Second, the computations of our model could be easily parallelized during training, because convolutional layers do not have time dependency as in LSTM layers, and gating units also work independently. The experiments on SemEval datasets demonstrate the efficiency and effectiveness of our models. 1

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The transformation from traditional manufacturing to intelligent manufacturing intrigues the profound and lasting effect on the future manufacturing worldwide. Industry 4.0 was proposed for advancing manufacturing to realize short product life cycles and extreme mass customization in a cost‐efficient way. As the heart of Industry 4.0, smart factory integrates physical technologies and cyber technologies and makes the involved technologies more complex and precise in order to improve performance, quality, controllability, management, and transparency of manufacturing processes. So far, leading manufacturers have begun the journey toward implementing smart factory. However, most firms still lack insight into the challenges and resources for implementing smart factory. As such, this paper identifies the requirements and key challenges, investigates available new technologies, reviews existing studies that have been done for smart factory, and further provides guidance for manufacturers to implementing smart factory in the context of Industry 4.0.