Semantic Simplification for Sentiment Classification (2022)

Abstract: Recent work on document-level sentiment classification has shown that the sentiment in the original text is often hard to capture, since the sentiment is usually either expressed implicitly or shifted due to the occurrences of negation and rhetorical words. To this end, we enhance the original text with a sentiment-driven simplified clause to intensify its sentiment. The simplified clause shares the same opinion with the original text but expresses the opinion much more simply. Meanwhile, we employ Abstract Meaning Representation (AMR) for generating simplified clauses, since AMR explicitly provides core semantic knowledge, and potentially offers core concepts and explicit structures of original texts. Empirical studies show the effectiveness of our proposed model over several strong baselines. The results also indicate the importance of simplified clauses for sentiment classification.

## introduction as a critical application of natural language processing, document-level sentiment classification has received considerable attention during the last two decades with the underlying assumption that the entire text has an overall polarity. in the literature, previous studies focus on predicting the overall sentiment from original text using either statistical @xcite @xcite or neural models @xcite @xcite . however, as shown in figure 1 (a), the overall sentiment of the original text is often hard to capture, since the overall sentiment is usually either expressed implicitly or shifted due to the occurrences of negation and rhetorical words. to address above challenges in the original text, both attention-based @xcite @xcite and rhetorical structure-based approaches (li et al., ## related work in this study, we introduce two related topics of this study: document-level sentiment classification and text simplification. ## method in this study, we aim to predict the polarity of a given document with its original text and the simplified clause. as shown in figure 2 , we first employs a sequence-to-structure network to generate the amr-based semantic graphs from sentences in the original text. we then use a simplified graph extraction model to merge the sentence-level semantic graphs and extract a document-level simplified semantic graph. thirdly, we use a structureto-sequence network to generate the simplified clause from the simplified semantic graph. afterward, we integrate the simplified clause and original review text for sentiment classification. in the following, we will illustrate these components of the proposed model, and then discuss the objective function and training process. ## experimentation in this section, we introduce the datasets used for evaluation and the baseline methods employed for comparison. we then report the experimental results conducted from different perspectives, and analyze the effectiveness of the proposed model with different factors. ## analysis and discussion in this section, we give some analysis and discussion to show the importance of the simplified clause for sentiment classification. note that, the results in this section are the average of all the domains. ## conclusion in this paper, we enhance the original text with a simplified clause for document-level sentiment classification. the simplified clause shares the same opinion with the original text but expresses the opinion much more simply. meanwhile, we employ amr for generating the simplified clause, since amr potentially offers core concepts and explicit structures from the original text. we then integrate the simplified clause with original text for sentiment classification. empirical studies demonstrate that our model significantly advances the state-of-the-art performance on several benchmark datasets. the results also indicate the simplified clause is very useful for sentiment classification.