Fine-Grained Sentiment Analysis for Enhanced Financial Distress Prediction

Abstract

Sentiment analysis aims to identify the sentiment polarity of specific aspects within given sentences or comments, and aspect-based sentiment analysis is considered a fundamental task in sentiment analysis. With practical applications in areas such as product reviews, food delivery evaluations, and public opinion monitoring, sentiment analysis plays a crucial role. This paper focuses on the application of fine-grained sentiment analysis in financial distress prediction (FDP) to enhance early warnings of the management status of companies. In previous studies, there has been a narrow emphasis on using document-level sentiment analysis to extract overall sentiment from text, overlooking the semantic nuances conveyed by sentiments. Therefore, this paper aims to extract fine-grained sentiments from the Management Discussion & Analysis (MD&A) of Chinese listed companies. The proposed model is based on a two-step framework, consisting of an unsupervised aspect-level financial sentiment extraction phase and a model validation phase. Specifically, the former is built on a deep learning model with an attention mechanism, conducting unsupervised aspect extraction, aspect identification, and aspect-level sentiment classification in a sequential manner to obtain fine-grained sentiments. The latter is responsible for evaluating the effectiveness of the newly acquired features on benchmark machine learning models, including SVM, DT, LR, CNN, and DNN. Experimental results reveal that MD&A predominantly covers eight types of aspects, including ownership, business scope, development, capital, sales, management, prizes, and probability. Additionally, it has been observed that fine-grained sentiment features can enhance the performance of FDP. This study represents a significant innovation in existing literature, being the first to introduce aspect-level financial sentiment analysis into the realm of FDP.