Cross-Domain Sentiment Classification using Semantic Representation (2022)

Abstract: Previous studies on cross-domain sentiment classification depend on the pivot features or utilize the target data for representation learning, which ignore the semantic relevance between different domains. To this end, we exploit Abstract Meaning Representation (AMR) to help with cross-domain sentiment classification. Compared with the textual input, AMR reduces data sparsity and explicitly provides core semantic knowledge and correlations between different domains. In particular, we develop an algorithm to construct a sentiment-driven semantic graph from sentence-level AMRs. We further design two strategies to linearize the semantic graph and propose a text-graph interaction model to fuse the text and semantic graph representations for cross-domain sentiment classification. Empirical studies show the effectiveness of our proposed model over several strong baselines. The results also indicate the importance of the proposed sentiment-driven semantic graph for cross-domain sentiment classification.

## introduction as an essential task in natural language processing, sentiment classification has gained great attention from both industry and academia with its wild-spread applications. while, a large number of review domains (e.g., product categories in amazon) make it intractable to manually annotate enough data in each domain for training domainspecific models. thus developing automatically cross-domain methods is imperative in this area. recent efforts on cross-domain sentiment classification can be separated into three categories: features-based approaches, discriminator-based approaches, and pretrained-based approaches. feature-based approaches @xcite ; yu * corresponding author and @xcite utilize a key intuition that domain-specific features could be aligned with the help of domain invariant features. meanwhile, discriminator-based approaches @xcite @xcite aim to determine the diversity between domains and predict the polarities of instances holistically. more recently, pre-trained language models are proposed to learn the crossdomain representations with mixed classification and masked language models @xcite @xcite . despite giving strong empirical results, previous studies ignore the semantic relevance between different domains. as shown in figure 1 , the expression of reviews from different domains are quite different, but they have the similar abstract meaning representation (amr) @xcite graphs. to this end, we propose a novel framework with amr for cross-domain sentiment classification. amr-based semantic graph models the review sentences using a rooted directed acyclic graph, which highlights its main concepts and semantic relations while abstracting away function words @xcite @xcite . it can thus potentially offer core concepts and explicit structures needed for aggregating the meaning of texts in different domains. in addition, we propose a sentiment-driven graph construction algorithm to better handle long review text and to make better use of the sentimental information. especially, the process of the construction algorithm can be separated into two stages: document-level semantic graph construction from the sentence-level amr graphs, and enriching the document-level semantic graph with sentimental information. we further design two strategies to linearize the sentiment-driven semantic graph: dfs-based linearization is closely related to the way natural language syntactic trees are linearized, and bfsbased linearization enforces a locality principle by which things belonging together are close to each other in the flat representation. afterward, we propose a text-graph interaction model to fuse the text and the linearized sentiment-driven semantic graph representations for cross-domain sentiment classification. the detailed evaluation shows that our model significantly advances the performance on several benchmark datasets. the results also show that the proposed sentiment-driven semantic graph is beneficial for cross-domain sentiment classification. ## related works in this part, we introduce two related topics of this study: cross-domain sentiment classification and abstract meaning representation. ## sentiment-driven semantic graph construction as shown in figure 2 , we aim to construct a document-level sentiment-driven semantic graph from the sentence-level amr graphs. given a review consisting multiple sentences, we apply an off-the-shelf parser 1 of amr parsing to process these sentences. afterward, we construct the document-level sentiment-driven semantic graph from these sentence-level semantic graphs. in particular, the process of the documentlevel sentiment-driven semantic graph construction can be separated into two stages: documentlevel semantic graph construction, and enriching the semantic graph with sentimental information. ## cross-domain sentiment classification with text and graph interactions our model takes a review text and the corresponding sentiment-driven semantic graph as input. as shown in figure 3 , we first learn the text representation, we then learn the semantic graph representation with different linearization techniques. thirdly, we employ a text-graph interaction model with self-attention and gate mechanism to learn the fused representation among the text and semantic graph representations. afterwards, we predict the polarity based on the fused representation. ## experiments in this section, we first introduce the datasets used for evaluation, and the baseline methods employed for comparison. in addition, we report the experimental results conducted from different perspectives, and analyze the effectiveness of proposed model with different factors. ## analysis and discussion in this section, we give analysis and discussion to show the importance of sentiment-driven semantic graph for cross-domain sentiment classification. ## conclusion in this study, we employ amr-based semantic representation for cross-domain sentiment classification. amr can reduce data sparsity, and explicitly provides core semantic knowledge and correlations among different domains. in particular, we develop an algorithm to construct a sentimentdriven semantic graph from sentence-level amrs. we further design two strategies to linearize the semantic graph and propose a text-graph interaction model to fuse the text and semantic graph representations for cross-domain sentiment classification. experimental results show the superiority of using the sentiment-driven semantic representations on cross-domain sentiment classification.