Embedding the ability of sentiment analysis in smart devices is especially challenging because sentiment analysis relies on deep neural networks, in particular, convolutional neural networks. The paper presents a novel hardware-friendly detector of image polarity, enhanced with the ability of saliency detection. The approach stems from a hardware-oriented design process, which trades off prediction accuracy and computational resources. The eventual solution combines lightweight deep-learning architectures and post-training quantization. Experimental results on standard benchmarks confirmed that the design strategy can infer automatically the salient parts and the polarity of an image with high accuracy. Saliency-based solutions in the literature prove impractical due to their considerable computational costs; the paper shows that the novel design strategy can deploy and perform successfully on a variety of commercial smartphones, yielding real-time performances.

将情感分析的能力嵌入智能设备尤其具有挑战性，因为情感分析依赖于深度神经网络，尤其是卷积神经网络。本文提出了一种新型的硬件友好的图像极性检测器，并通过显着性检测功能进行了增强。该方法源于面向硬件的设计过程，该过程折衷了预测准确性和计算资源。最终的解决方案结合了轻量级的深度学习架构和训练后量化。在标准基准上的实验结果证实，该设计策略可以自动推断图像的显着部分和极性。文献中基于显着性的解决方案由于计算量巨大而被证明是不切实际的。