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Multi-Aspect Attention Model for Aspect-based Sentiment Classification Using Deep Learning

机译：基于深度学习的基于方面的情感分类的多方面注意模型

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摘要

Aspect-based sentiment classification (ABSC) (also called fine-grained sentiment analysis) is an essential and challenging task among sentiment analysis tasks. Aspect level sentiment analysis overcomes the limitation of the document and sentence level when multiple aspects appear in a review. Recently, neural network approaches like LSTM has achieved better results in ABSC than traditional machine learning algorithms. Aspect extraction and polarity detection for specific aspects in the review becoming an important task in aspect level sentiment analysis. Therefore, the paper aims to predict the sentiment polarity of each aspect in the review into 3 classes by developing the multi-aspect attention (MAA) model and combine it with the BiLSTM neural network, called MAA-BLSTM. Unlike unidirectional LSTM, the BiLSTM network runs the input in two directions: forward and backward, therefore, it can understand the context better than LSTM. In this paper, the hyperparameter tuning approach is also considered to be the high-performing model. Experiments are tested on restaurant and laptop data from SemEval (2014, 2015, and 2016) datasets and compare the result with other LSTM-based methods. Finally, the result proves that the accuracy of the proposed sentiment model reaches 89.9 on the restaurant dataset and 82.1 on the laptop dataset which are higher than other LSTM approaches.
机译：基于方面的情感分类（ABSC）（也称为细粒度情感分析）是情感分析任务中一项必不可少且具有挑战性的任务。当评论中出现多个方面时，方面级别的情感分析克服了文档和句子级别的限制。最近，像LSTM这样的神经网络方法在ABSC中取得了比传统机器学习算法更好的结果。审查中特定方面的方面提取和极性检测已成为方面级别情感分析中的一项重要任务。因此，本文旨在通过开发多方面注意（MAA）模型并将其与称为MAA-BLSTM的BiLSTM神经网络相结合，将评论中各个方面的情感极性预测为3类。与单向LSTM不同，BiLSTM网络在两个方向上运行输入：向前和向后，因此，与LSTM相比，它可以更好地理解上下文。在本文中，超参数调整方法也被认为是高性能模型。对来自SemEval（2014、2015和2016）数据集的餐厅和笔记本电脑数据进行实验测试，并将结果与​​其他基于LSTM的方法进行比较。最后，结果证明，所建立的情感模型在饭店数据集上的准确性达到89.9，在笔记本电脑数据集上达到82.1，比其他LSTM方法更高。

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