In the current literature, many methods have been devised for sentiment quantification. In this work, we propose AspEntQuaNet, one of the first methods for aspect-based sentiment quantification. It extends the state-of-the-art QuaNet deep learning method for sentiment quantification in two ways. First, it considers aspects and ternary sentiment quantification concerning these aspects instead of binary sentiment quantification. Second, it improves on the results of QuaNet with an entropy-based sorting procedure instead of multisorting. Other sentiment quantification methods have also been adapted for ternary sentiment quantification instead of binary sentiment quantification. Using the modified version of the SemEval 2016 dataset for aspect-based sentiment quantification, we show that AspEntQuaNet is superior to all other considered existing methods based on obtained results for various aspect categories. In particular, AspEntQuaNet outperforms QuaNet often by a factor of 2 on all considered evaluation measures.

中文翻译：

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基于方面的情感量化


在当前的文献中，已经设计了许多用于情感量化的方法。在这项工作中，我们提出了 AspEntQuaNet，它是基于方面的情感量化的首批方法之一。它以两种方式扩展了最先进的 QuaNet 深度学习方法以进行情感量化。首先，它考虑方面和与这些方面有关的三元情感量化，而不是二元情感量化。其次，它通过基于熵的排序过程而不是多重排序改进了 QuaNet 的结果。其他情感量化方法也适用于三元情感量化而不是二元情感量化。使用 SemEval 2016 数据集的修改版本进行基于方面的情感量化，我们根据获得的各种方面类别的结果表明，AspEntQuaNet 优于所有其他考虑的现有方法。特别是，在所有考虑的评估指标上，AspEntQuaNet 的性能通常比 QuaNet 高出 2 倍。