Phrase-Aware Financial Sentiment Analysis Based on Constituent Syntax

Financial sentiment analysis is a fine-grained sentiment analysis task that needs to predict the sentiment value toward a given target entity. Recently, dependency-based graph neural networks have been introduced for target-based sentiment analysis. However, financial sentiment analysis with implicit sentiment expression is more challenging than target-based explicit sentiment analysis, requiring a deep understanding of the complex association between the sentiment clue in context and the target entity. In previous work related to financial sentiment analysis, most methods focused on learning the simple word-to-word relations between the contextual words and the target entity based on the dependency tree of the sentence, ignoring the exploitation of span-boundary information and phrase-level syntactic knowledge with regard to the target entity. In this paper, we perform financial implicit sentiment analysis by taking phrases as basic semantic units and proposing a graph attention network (
$PhraseGAT$
) based on the constituent tree to leverage the phrase syntactic knowledge. To enhance the information flow between the nodes in the graph, we construct a heterogeneous graph based on the constituent tree and encode higher-order neighbor information. In addition, we introduce a multi-edge-type graph attention network (
$MET$
-
$GAT$
) to take full consideration of syntax and semantic interactions for the final prediction on the sentiment value of the target entity. Our proposed approach achieves 85.56% and 84.37% cosine similarity on public benchmark HEADLINE and MICROBLOG datasets, outperforms several strong baselines and achieves new state-of-the-art performance, verifying its effectiveness.