Target-Guided Structured Attention Network for Target-Dependent Sentiment Analysis

Target-Guided Structured Attention Network for Target-Dependent Sentiment Analysis Ji Zhang Chengyao Chen Pengfei Liu Chao He Cane Wing-Ki Leung Wisers AI Lab, Wisers Information Limited, HKSAR, China {jasonzhang, stacychen, chaohe, caneleung}@wisers.com, ppfliu@gmail.com Abstract Target-dependent sentiment analysis (TDSA) aims to classify the sentiment of a text towards a given target. The major challenge of this task lies in modeling the semantic relatedness between a target and its context sentence. This paper proposes a novel Target-Guided Struc- tured Attention Network (TG-SAN), which captures target-related contexts for TDSA in a fine-to-coarse manner. Given a target and its context sentence, the proposed TG-SAN first identifies multiple semantic segments from the sentence using a target-guided structured atten- tion mechanism. It then fuses the extracted segments based on their relatedness with the target for sentiment classification. We present comprehensive comparative experiments on three benchmarks with three major findings. First, TG-SAN outperforms the state-of-the-art by up to 1.61% and 3.58% in terms of accu- racy and Marco-F1, respectively. Second, it shows a strong advantage in determining the sentiment of a target when the context sentence contains multiple semantic segments. Lastly, visualization results show that the attention scores produced by TG-SAN are highly interpretable 1 Introduction Target-dependent sentiment analysis (TDSA) is an actively studied research topic with the aim to determine the sentiment polarity of a text towards a specific target. For example, given a sentence ‘‘the food is so good and so popular that waiting can really be a nightmare’’, the target-dependent sentiments of food and waiting are positive and negative, respectively. The major challenge of TDSA lies in modeling the semantic relatedness between the target and its context sentence (Tang et al., 2016a; Chen et al., 2017). Most recent progress in this area benefits from the attention mechanism, which captures the relevance between the target and every other word in the sentence. Based on such word-level correlations, several models have already been proposed for constructing target-related sentence representations for sentiment prediction (Wang et al., 2016; Tang et al., 2016b; Liu and Zhang, 2017; Yang et al., 2017; Ma et al., 2017). One important underlying assumption in exist- ing attention-based models is that words can be used as independent semantic units for model- ing the context sentence when performing TDSA. This assumption neglects the fact that a sentence is oftentimes composed of multiple semantic seg- ments, where each segment may contain multiple words expressing a certain meaning or senti- ment collectively. Furthermore,different semantic segments may even contribute differently to the sentiment of a certain target. Figure 1 shows an example of a restaurant review, which contains two salient semantic segments (highlighted in blue). Intuitively, a TDSA model should be able to identify both segments and determine that the second one is more relevant to the writer’s sen- timenttowards the target[waiting]. Existing meth- ods, however, would only attend important words (highlighted in red) such as ‘‘good’’, ‘‘popular’’, ‘‘really’’, and ‘‘nightmare’’ individually through the aforementioned assumption. We hypothesize that the ability to uncover mul- tiple semantic segments and their …

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