Identification of fact-implied implicit sentiment based on multi-level semantic fused representation

Abstract

Sentiment can be expressed in an explicit or implicit manner. Most of the current studies on sentiment analysis focus on the identification of explicit sentiment but ignore the implicit. According to our statistics during data labeling in previous work, nearly a third of subjective sentences contain implicit sentiment, and 72% of the implicit sentiment sentences are fact-implied ones. We analyze the characteristics of the sentences that express fact-implied implicit sentiment and consider that fact-implied implicit sentiment is usually affected by its sentiment target, context semantic background and its own sentence structure. This paper focuses on the recognition of fact-implied implicit sentiment at the sentence level. A multi-level semantic fusion method is proposed to learn the features for identification based on representation learning. Three features in different levels are learned from the corpus, namely, sentiment target representation at the word level, structure embedded representation at the sentence level and context semantic background representation at the document level. We manually construct a fact-implied implicit sentiment corpus in Chinese, and experiments on the datasets show that the proposed method can effectively recognize fact-implied implicit sentiment sentences.