Implicit sentiment analysis based on affective knowledge and event information

Abstract

In recent years, implicit sentiment analysis, which aims to detect sentiment of a sentence that don’t contain obvious sentiment words, has become an attractive research topic. This paper focuses on event-centric implicit sentiment analysis, which utilizes the sentiment-aware event to infer the sentiment polarity of the sentence. Existing event-based implicit sentiment analysis methods typically treat entities or noun phrases in the text as events, or model contextual information to indirectly infer events using sophisticated models, but these methods fail to fully capture event information. To address these issues, this paper defines events as <subject, predicate, object>. Based on this event representation, neural tensor network was used to model the interaction between event elements and extract high-level semantic features of events. In addition, a novel affective enhanced graph model was proposed to capture sentiment-related dependencies between context words. Furthermore, this paper considers the case where a sentence contains multiple events, and constructs an event-centric implicit sentiment analysis dataset, where each sentence contains at least one event triplet. Experimental results on the constructed dataset demonstrate the effectiveness of our proposed approach.