Abstract: Implicit sentiment analysis is to detect the sentiment of sentences that do not contain obvious sentiment words.This paper focuses on event-centric sentiment analysis,which is to infer sentiment polarity from the events described in the sentence.In event-centric sentiment analysis,existing methods either treat noun phrases in the text as events,or adopt complex models to model events,fail to model event information sufficiently,and fail to consider events that contain multiple events.In order to solve the above problems,it is proposed to represent events in the form of event triples〈subject,predicate,object〉.Based on this event representation,an event-enhanced semantic-based sentiment analysis model(MEA) is further proposed to detect the sentiment of texts.In this paper,syntactic information is used to capture the relationship of event triples,and attention mechanism is used to model the relationship between events according to the contribution of each event to the sentence.At the same time,a bidirec-tional long-short-term memory network(Bi-LSTM) is used to model the contextual information of sentences,and a multi-level orthogonal attention mechanism is used to capture the difference of attention weights under different polarities,which can be used as a significant discriminative feature.Finally,according to the importance of event features and sentence features,they are assigned different weight ratios,and they are fused to obtain the final sentence representation.Furthermore,this paper proposes a dataset for event-enhanced sentiment analysis(MEDS),where each sentence is labeled with event triplet representations and sentiment polarity labels.Research shows that the proposed model outperforms existing baseline models in self-built datasets.

Key words: Event-based sentiment analysis, Representation learning, Sentiment analysis, Graph convolutional neural network, Attention mechanism