  Mining financial text documents and understanding the sentiments of
individual investors, institutions and markets is an important and challenging
problem in the literature. Current approaches to mine sentiments from financial
texts largely rely on domain specific dictionaries. However, dictionary based
methods often fail to accurately predict the polarity of financial texts. This
paper aims to improve the state-of-the-art and introduces a novel sentiment
analysis approach that employs the concept of financial and non-financial
performance indicators. It presents an association rule mining based
hierarchical sentiment classifier model to predict the polarity of financial
texts as positive, neutral or negative. The performance of the proposed model
is evaluated on a benchmark financial dataset. The model is also compared
against other state-of-the-art dictionary and machine learning based approaches
and the results are found to be quite promising. The novel use of performance
indicators for financial sentiment analysis offers interesting and useful
insights.
