KC - ISA : An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features (2022)

Abstract: Sentiment analysis has always been an important research direction in natural language processing. The research can be divided into explicit sentiment analysis and implicit sentiment analysis according to whether there are sentiment words in language expression. There have been many research results in explicit sentiment analysis. However, implicit sentiment analysis is rarely studied. Compared with explicit sentiment expression, implicit sentiment expression usually omits a lot of knowledge and common sense, and context also has an important impact on implicit sentiment expression. In this paper, we use a knowledge graph to supplement implicit sentiment expression and propose a novel Implicit Sentiment Analysis model combining Knowledge enhancement and Context features (dubbed KC-ISA). The KC-ISA model can effectively integrate external knowledge and contextual features by the coattention mechanism. Finally, we conduct experiments on the SMP2019 implicit sentiment analysis dataset. Moreover, to verify the generality of the model, we also conduct experiments on two common sentiment analysis datasets. The results on three datasets show that our proposed KC-ISA model can achieve better results on text sentiment analysis.

## introduction with the rapid development of the internet, the internet has become the media form with the widest audience and the fastest reflection of social dynamics in today's society. online social media has become an excellent platform in which all users can easily participate, share and communicate. therefore, a large number of texts that reflect the user's subjective consciousness have begun to appear, and the texts with the user's sentiment tendencies are also increasing. text sentiment analysis is the process of analyzing, summarizing, and reasoning subjective senti- * corresponding author ment texts. from the expression level of text, it can be divided into explicit sentiment text and implicit sentiment text according to whether the text contains explicit sentimental words. @xcite firstly divided sentiment into subjective opinion and factual implication opinion. the former is a subjective statement that gives the polarity of sentiment, while the latter is an implicit expression of sentiment through an objective statement. @xcite defined implicit sentiment sentences as "language fragments that express subjective sentiment but do not contain explicit sentiment words". as the basic research of sentiment analysis, the analysis of explicit sentiment text has abundant relevant research results. however, the emotions reflected by people's behavior and feelings towards objective things are often rich and abstract and are usually expressed in subtle ways. for example, using objective statements to express one's sentiment, or using rhetorical expressions to describe them vividly, is called implicit sentiment expression. that means the implicit sentiment expression depends on something other than the sentiment words. so implicit sentiment analysis first needs to discover these dependencies. according to @xcite , about 30% of sentences contain implicit sentiment. by reviewing the relevant paper @xcite and analyzing the data, we summarized that implicit sentiment sentences have the following three distinct dependencies: e1-s1: this orange is only sold for 1 yuan a pound. e1-s2: it was so cheap! e2-s1: this orange is only sold for 1 yuan a pound. e2-s2: it must be very bad! e1-s1 and e2-s1 are the target sentences for sentiment analysis. in e1-s1, because the price of oranges is low, the speaker has the intention to buy and expresses a positive sentiment. in e2-s1, because the price is too low, the speaker doubts the taste of orange and expresses a negative sentiment. the same sentence expresses different sentiments due to the difference between e1-s2 and e2-s2. therefore, contextual information is an important factor affecting implicit sentiment expression. if we want to accurately analyze implicit sentiment expression, it is crucial to integrate contextual features. e3: in the race, he runs like a cheetah. e4: in the race, he runs like a turtle. e3 and e4 are exactly the same except for the sentiment target, but the first sentence is positive and the second sentence is negative. the two sentences express completely opposite implicit sentiment only by the change of the sentiment target, indicating that the implicit sentiment is also closely related to the sentiment target. therefore, to address the three distinct dependencies of implicit sentiment expression, we need model context, sentiment target, and external knowledge. especially, for knowledge and common sense dependency, we introduce a knowledge graph as external common sense to supplement implicit sentiment sentences to enrich the expression of sentiments. inspired by the coattention @xcite mechanism, in this paper, we propose kc-isa, a novel implicit sentiment analysis model combining knowledge enhancement and context features, which can effectively integrate external knowledge and contextual features to com-plement sentiment expressions. moreover, by the pre-trained model, kc-isa can utilize external knowledge and contextual features more effectively. extensive experiments show that our proposed kc-isa model can effectively improve the accuracy of implicit sentiment analysis. in summary, the main contributions of this paper are as follows: first, we propose kc-isa model for three distinct dependencies of implicit sentiment analysis. moreover, we combined kc-isa model with the pre-trained model bert as kc-isa-bert. kc-isa model can effectively integrate contextual features and incorporate external knowledge into implicit sentiment analysis. second, we conduct experiments on implicit sentiment dataset, and the results show that our proposed model can effectively improve the accuracy of implicit sentiment analysis. third, considering the real review data is a mixture of explicit and implicit data, we further conducted experiments on two common sentiment analysis datasets. the experimental results further validate the effectiveness and general applicability of our kc-isa model. the datasets and code have been uploaded to https://github.com/anonymouscoling2022/kcisa 2 related work related work mainly includes two parts, one is implicit sentiment analysis, and the other is integrating external knowledge in natural language processing tasks. ## proposed model we summarized three dependencies of implicit sentiment expression earlier, i.e. context dependency, sentiment target dependency, and knowledge and common sense dependency. since there are no explicit sentiment words in implicit sentiment sentences as a guide, traditional sentiment analysis methods are not very effective. therefore, we proposed kc-isa model for the three distinct dependencies. the model can effectively model the relationship between the target sentence and the context, and incorporate external knowledge, which can effectively enrich the implicit sentiment expression of the sentence and improve the accuracy of implicit sentiment analysis. ## conclusion in this paper, we summarized three distinct dependencies of implicit sentiment analysis: context dependency, sentiment target dependency, and knowledge and common sense dependency. and the xlore bilingual encyclopedia knowledge graph was selected as an external knowledge base to supplement a large amount of common sense omitted in implicit emotional sentences. finally, we proposed kc-isa model for the distinct dependencies of implicit sentiment analysis, and combined the pre-trained model bert to proposed kc-isa-bert, which can effectively integrate contextual features and external knowledge. to verify the effectiveness and generality of the model, experiments are conducted on an implicit sentiment analysis dataset and two common sentiment analysis datasets, and the experiments show that our model outperforms baselines.