Abstract

Sentiment dictionary is of great value to sentiment analysis, which is used widely in sentiment analysis compositionality. However, the sentiment polarity and intensity of the word may vary from one domain to another. In this paper, we introduce a novel approach to build domain-dependent sentiment dictionary, SentiDomain. We propose a weak supervised neural model that aims at learning a set of sentiment clusters embedding from the sentence global representation of the target domain. The model is trained on unlabeled data with weak supervision by reconstructing the input sentence representation from the resulting representation. Furthermore, we also propose an attention-based LSTM model to address aspect-level sentiment analysis task based on the sentiment score retrieved from the proposed dictionary. The key idea is to weight-down the non-sentiment parts among aspect-related information in a given sentence. Our extensive experiments on both English and Chinese benchmark datasets have shown that compared to the state-of-the-art alternatives, our proposals can effectively improve polarity detection.

中文翻译：

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构造用于情感分析的与领域相关的情感字典

摘要

情感词典对情感分析具有重要的参考价值，广泛用于情感分析的成分分析中。但是，单词的情感极性和强度可能会在一个域之间变化。在本文中，我们介绍了一种新的方法来构建依赖于域的情感词典SentiDomain。我们提出了一种弱监督神经模型，旨在学习从目标域的句子全局表示中嵌入的一组情感簇。通过从结果表示中重建输入语句表示，可以在无标签数据的弱监督下训练模型。此外，我们还提出了一种基于注意力的LSTM模型，用于基于从拟议词典中检索到的情感分数来解决方面水平的情感分析任务。关键思想是减少给定句子中与方面相关的信息中的非情感部分。我们对中英文基准数据集进行的广泛实验表明，与最新的替代方法相比，我们的建议可以有效地改善极性检测。