基于多事件语义增强的情感分析

Sentiment Analysis Based on Multi-event Semantic Enhancement

隐式情感分析是检测不包含明显情感词的句子的情感。文中集中于以事件为中心的情感分析,该任务是通过句子中描述的事件推断其情感极性。在以事件为中心的情感分析中,现有方法要么将文本中名词短语看作事件,要么采用复杂的模型建模事件,未能充分建模事件信息,并且没有考虑到包含多个事件的情况。为解决以上问题,提出将事件表示为事件三元组〈主语,谓语,宾语〉的形式,基于这种事件表示,进一步提出基于事件增强语义的情感分析模型MEA来检测文本的情感。文中利用句法信息捕获事件三元组的关系,根据每个事件对句子贡献程度的不同,采用注意力机制建模事件之间的关系。与此同时,采用双向长短时记忆网络建模句子的上下文信息,并采用多级性正交注意力机制捕获不同极性下注意力权重的差异,这可以作为显著的判别特征。最后,依据事件特征和句子特征的重要程度为其分配不同的权重比例,并将它们融合得到最终的句子表示。此外,文中还提出一个用于事件增强情感分析的数据集MEDS,其中每条句子都标有事件三元组表示和情感极性标签。研究表明,在自建的数据集中,所提模型优于现有的基线模型。

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.