Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment Analysis

Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018), pages 181–189 Brussels, Belgium, October 31 - November 1, 2018. c⃝2018 Association for Computational Linguistics 181 Abstract Aspect-level sentiment analysis aims to identify the sentiment of a specific target in its context. Previous works have proved that the interactions between aspects and the contexts are important. On this basis, we also propose a succinct hierarchical attention based mechanism to fuse the information of targets and the contextual words. In addition, most existing methods ignore the position information of the aspect when encoding the sentence. In this paper, we argue that the position-aware representations are beneficial to this task. Therefore, we propose a hierarchical attention based position-aware network (HAPN), which introduces position embeddings to learn the position-aware representations of sentences and further generate the target-specific representations of contextual words. The experimental results on SemEval 2014 dataset show that our approach outperforms the state-of-the- art methods. 1 Introduction Aspect-level sentiment analysis is a fine-grained task in sentiment analysis, which aims to identify the sentiment polarity (i.e., negative, neutral, or positive) of a specific opinion target expressed in a comment/review by a reviewer. For example, given a sentence “The price is reasonable although the service is poor”, the sentiment polarity for aspects “price” and “service” are positive and negative respectively. Traditional methods for aspect-level sentiment analysis mainly focus on designing a set of features (such as bag-of-words, sentiment lexicons, and linguistic features) to train a classifier for sentiment classification (Kiritchenko et al., 2014; Wagner et al., 2014; Vo and Zhang, 2015). However, such kind of feature engineering work often relies on human ingenuity, which is a time- consuming process and lacks generalization. In recent years, more and more neural network based models have been proposed and obtained the state- of-the-art results (Wang et al., 2016; Tang et al., 2016a;2016b; Chen et al,. 2017; Ma et al., 2017; Tay et al., 2017; Zheng et al., 2018; Huang et al., 2018). As previous research (Jiang et al., 2011) reveals that 40% of sentiment classification errors are caused by not considering targets in sentiment classification, recent works tend to focus on fusing the information of the targets and the contexts. Wang et al. (2016) and Tang et al. (2016a) both concatenated the aspect embeddings and embeddings of each word as inputs to a LSTM based model so as to introduce the information of the target into the model. Tay et al. (2017) adopted circular convolution and circular correlation to model the similarity between aspect and contextual words. Ma et al. (2017) and Zheng et al. (2018) both employed a bidirectional attention operation to achieve the representations of targets and contextual words determined by each other. Huang et al. (2018) introduced an attention-over-attention based network to model the aspects and contexts in a joint way and explicitly capture the interaction between aspects and the context. As described above, the existing studies show that the interactions between aspects and the context are important to the aspect-level …

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