FinSenticNet: A Concept-Level Lexicon for Financial Sentiment Analysis
Kelvin Du, Frank Xing, Rui Mao, Erik Cambria
Abstract
Sentiment lexicons are important tools for research involving opinion mining and sentiment analysis. They are highly inter-operable, and address critical limitations of learning-based or large language model-based sentiment analysis, providing better reproducibility and explainability. Existing financial sentiment lexicons, manually crafted or automatically constructed, primarily comprise single-word entries despite the fact that jargon, terminologies, and collocations in finance are often multi-word expressions. To address this gap, we present FinSenticNet, a concept-level domain-specific lexicon specifically designed for financial sentiment analysis, where over 65% entries are multi-word expressions. Our construction approach is semi-supervised: the framework consists of a concept parser, a sentiment seeds generation module, and a semantic graph construction module. Each concept (graph node) is subsequently classified in terms of its polarity using the Label Propagation Algorithm and Graph Convolutional Network. Compared to other financial sentiment lexicons, FinSenticNet captures domain-specific language features and has a broader coverage. We demonstrate this with superior evaluation results, i.e., sentiment analysis accuracy and F-scores, on multiple well-received benchmark datasets.