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"Sentiment Analysis": models, code, and papers

Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device

Jan 28, 2020
Baohua Sun, Lin Yang, Hao Sha, Michael Lin

Recent years NLP research has witnessed the record-breaking accuracy improvement by DNN models. However, power consumption is one of the practical concerns for deploying NLP systems. Most of the current state-of-the-art algorithms are implemented on GPUs, which is not power-efficient and the deployment cost is also very high. On the other hand, CNN Domain Specific Accelerator (CNN-DSA) has been in mass production providing low-power and low cost computation power. In this paper, we will implement the Super Characters method on the CNN-DSA. In addition, we modify the Super Characters method to utilize the multi-modal data, i.e. text plus tabular data in the CL-Aff sharedtask.

* 9 pages, 2 figures, 6 tables. Accepted by AAAI 2020 Affective Content Analysis Workshop 

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