Pipelined Neural Networks for Phrase-Level Sentiment Intensity Prediction
Pipelined Neural Networks for Phrase-Level Sentiment Intensity Prediction
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DOI：
10.1109/taffc.2018.2807819
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发表时间：
2020-07
影响因子：
11.2
通讯作者：
Liang-Chih Yu;Jin Wang;Xuejie Zhang;K. R. Lai
中科院分区：
文献类型：
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作者：
Liang-Chih Yu;Jin Wang;Xuejie Zhang;K. R. Lai
语言修饰语如否定词(如NOT)、强化词(如Very)和情态动词(如Will)常用于表达观点。这些修饰语在识别多词短语的情感强度方面起着重要的作用，因为它们可能会导致所修饰的词的强度转移和极性反转。对这些修饰语对强度转换的影响进行适当的建模，可以极大地提高短语级情感强度预测的性能。为此，本文提出了两个以流水线方式组织的神经网络(NN)模型，以确定1)单个词的强度和2)修饰语的移动权重，该修饰语表示它们所修改的词的强度变化程度。然后，通过组合构成词的强度和短语内修饰语的移动权重来确定短语的强度。在度量词强度时，第一种神经网络模型引入隐含层作为过滤器，在预测过程中选择合适的相似种子词。自动词强度预测可以解决情感词典中未涵盖的词的未知强度问题。在学习修改器权重时，第二个神经网络模型同时考虑单个修改器的权重和修改器组的权重，以捕捉由它们引起的各种强度漂移效果。在SemEval-2016数据集上的实验表明，该方法对单词和多词短语都有较好的预测效果。
Linguistic modifiers such as negators (e.g., not), intensifiers (e.g., very) and modals (e.g., would) are commonly used in expressing opinions. These modifiers play an important role in recognizing the sentiment intensity of multi-word phrases because they may lead to an intensity shift and polarity reversal for the words they modify. Appropriately modeling the effect of such modifiers on the intensity shift can greatly improve the performance of phrase-level sentiment intensity prediction. To this end, this paper proposes two neural network (NN) models organized in a pipelined fashion to determine 1) the intensity of individual words and 2) the shift weights of modifiers representing the degrees of intensity change for the words they modify. The intensity of a phrase can then be determined by combining the intensity of the constituent word and the shift weight of the modifier within the phrase. When measuring the word intensity, the first NN model introduces a hidden layer as a filter to select appropriate similar seed words in the prediction process. Automatic word intensity prediction can address the unknown intensities of words not covered in sentiment lexicons. In learning the modifier weights, the second NN model considers both the weights of individual modifiers and groups of modifiers to capture various intensity shift effects caused by them. Experiments on a SemEval-2016 dataset showed that the proposed method yielded better prediction performance for both single words and multi-word phrases.