Improving Affective Event Classification with Multi-Perspective Knowledge Injection (2023)

Abstract: “In recent years, many researchers have recognized the importance of associating events withsentiments. Previous approaches focus on generalizing events and extracting sentimental in-formation from a large-scale corpus. However, since context is absent and sentiment is oftenimplicit in the event, these methods are limited in comprehending the semantics of the eventand capturing effective sentimental clues. In this work, we propose a novel Multi-perspectiveKnowledge-injected Interaction Network (MKIN) to fully understand the event and accuratelypredict its sentiment by injecting multi-perspective knowledge. Specifically, we leverage con-texts to provide sufficient semantic information and perform context modeling to capture thesemantic relationships between events and contexts. Moreover, we also introduce human emo-tional feedback and sentiment-related concepts to provide explicit sentimental clues from theperspective of human emotional state and word meaning, filling the reasoning gap in the senti-ment prediction process. Experimental results on the gold standard dataset show that our modelachieves better performance over the baseline models.”

## introduction affective event classification (aec) aims at predicting the sentiment polarity of the given event. we consider events that have positive effects on people who experience them as positive events. for instance, typically positive events include having a new crush, going to the bonfire, seeing a rainbow. on the contrary, events that have negative effects on people who experience them are treated as negative events, such as breaking a marriage, going to the funeral, hearing a loud noise. since events often trigger sentiments and sentiments are often implicit, recognizing affective events is of great values to various natural language processing applications, covering dialogue systems @xcite , question-answering systems @xcite , implicit sentiment analysis @xcite and opinion mining @xcite . the challenges of aec lie in the limited context and the implicit sentiment of the event. to be specific, we often rely on the context to analyze sentiments, but there is no rich context to understand the event. besides, traditional sentiment analysis methods rely on the occurrence of explicit sentiment words, but there are few explicit sentimental clues in the event. many previous approaches have been devoted to cope with the challenges by extracting sentimental information from a large-scale corpus @xcite @xcite . however, such attempts are not effective enough to understand the event and capture sentimental clues due to weak context modeling and insufficient sentimental information. we believe that this task would benefit from multi-perspective knowledge injection. specifically, context can provide additional semantic information to understand the event, and human emotional feedback as well as sentiment-related concepts can provide explicit sentimental information from two perspectives to fill the reasoning gap. figure 1 shows an example of an affective event, demonstrating the significance figure 1 : an example of an affective event for identifying the sentiment with the help of event contexts, human emotional feedback and sentiment-related concepts. in this paper, to cope with the aforementioned challenges, we propose a novel multi-perspective knowledge-injected interaction network (mkin) to thoroughly comprehend the event and precisely infer its sentiment. specifically, we utilize a pre-trained generative commonsense reasoning model to create event contexts and human emotional feedback of the event. meanwhile, a commonsense knowledge base and an emotion dictionary are adopted to retrieve sentiment-related concepts. then, we devise a multi-source text encoding module to encode these events and knowledge. to better integrate contextual information and sentimental clues, we construct a semantic and sentimental fusion module, which performs interaction as well as fusion of semantics and sentiments. finally, we introduce a classifier to accurately classify affective events. to evaluate the performance of mkin, we conduct extensive experiments on the gold standard dataset for aec. state-of-the-art performance is achieved by us compared with the baseline models. the main contributions of our work are summarized as follows: ## related work relevant work mainly includes two directions, one is affective event classification, and the other is incorporating external knowledge in sentiment analysis tasks. ## methodology the problem of the aec task could be formulated as follows. given an event tuple ⟨ agent: with the corresponding sentiment category, the goal of this task is to predict the sentiment distribution over three sentiment polarities. the overall architecture of our proposed model mkin is shown in figure 2 , which consists of four modules: knowledge acquisition module, multi-source text encoding module, semantic and sentimental fusion module, sentiment classification module. each one of the four modules will be elaborated in the rest of this section. ## experiments in this section we present the dataset, evaluation metrics, baseline models, model variants, and other experimental settings. ## results and analysis in this section we present model evaluation results, ablation study, and case study. ## conclusion and future work in this paper, we propose a novel multi-perspective knowledge-injected interaction network (mkin) for affective event classification. mkin models various aspects of information by considering contexts, human emotional feedback, and sentiment-related concepts, to fully comprehend the event as well as accurately predict its sentiment. to be more specific, in order to complement the semantic information of the event, we leverage context information and perform context modeling to capture the semantic association between the event and the context. to enhance the sentimental information of the event, we take advantage of human emotional feedback to provide sentimental clues from the perspective of people's emotional state. in addition, external sentiment-related concepts are introduced to enrich the word-level representations. both emotional state information and concept information fill the reasoning gap between events and sentiments. experiment results show that knowledge injection from all perspectives improve the model performance, and our model achieves 2.1% performance improvement over the state-of-the-art model on the gold standard dataset. for future work, to apply the model in a variety of natural language processing applications, we would like to explore event-centered sentiment analysis. affective event classification can be employed as an additional subtask to improve sentiment analysis.