Tree Communication Models for Sentiment Analysis

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3518–3527 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 3518 Tree Communication Models for Sentiment Analysis Yuan Zhang and Yue Zhang School of Engineering, Westlake University, China Institute of Advanced Technology, Westlake Institute for Advanced Study chuengyuenn@gmail.com, zhangyue@westlake.edu.cn Abstract Tree-LSTMs have been used for tree-based sentiment analysis over Stanford Sentiment Treebank, which allows the sentiment signals over hierarchical phrase structures to be cal- culated simultaneously. However, traditional tree-LSTMs capture only the bottom-up de- pendencies between constituents. In this pa- per, we propose a tree communication model using graph convolutional neural network and graph recurrent neural network, which allows rich information exchange between phrases constituent tree. Experiments show that our model outperforms existing work on bidirec- tional tree-LSTMs in both accuracy and effi- ciency, providing more consistent predictions on phrase-level sentiments. 1 Introduction There has been increasing research interest inves- tigating sentiment classification over hierarchical phrases (Tai et al., 2015; Zhu et al., 2015; Looks et al., 2017; Teng and Zhang, 2017). As shown in Figure 1, the goal is to predict the sentiment class over a sentence and each phrase in its constituent tree. There have been methods that classify each phrase independently (Li et al., 2015; McCann et al., 2017). However, sentiments over hierarchi- cal phrases can have dependencies. For example, in Figure 1, both sentences have a phrase “an awe- some day”, but the polarities of which are different according to their sentence level contexts. To better represent such sentiment dependen- cies, one can encode a constituency tree holisti- cally using a neural encoder. To this end, tree- structured LSTMs have been investigated as a dominant approach (Tai et al., 2015; Zhu et al., 2015; Gan and Gong, 2017; Yu et al., 2017; Liu et al., 2016). Such methods work by encoding hierarchical phrases bottom-up, so that sub con- stituents can be used as inputs for representing a 2 (1) 2 2 0 2 2 I had an awesome day winning the game 0 0 2 2 2 0 (2) I had an awesome day experiencing the tsunami * 0 0 0 0 0 0 0 0 * 0 -2 -1 -2 -2 -1 -1 -2 -2 -2 * * * * Figure 1: Examples of tree-based sentiment. constituent. However, they cannot pass informa- tion from a constituent node to its children, which can be necessary for cases similar to Figure 1. In this example, sentence level information from top- level nodes is useful for disambiguating “an awe- some day”. Bi-directional tree LSTMs provide a solution, using a separate top-down LSTM to aug- ment a tree-LSTM (Teng and Zhang, 2017). This method has achieved highly competitive accura- cies, at the cost of doubling the runtime. Intuitively, information exchange between tree nodes can happen beyond bottom-up and top- down directions. For example, direct communica- tion between sibling nodes, such as (“an awesome day”, “winning the game”) and (“an awesome day”, “experiencing …

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