This paper attempts to identify the importance of sentiment words in financial reports on financial risk. By using a financespecific sentiment lexicon, we apply regression and ranking techniques to analyze the relations between sentiment words and financial risk. The experimental results show that, based on the bag-of-words model, models trained on sentiment words only result in comparable performance to those on origin texts, which confirms the importance of financial sentiment words on risk prediction. Furthermore, the learned models suggest strong correlations between financial sentiment words and risk of companies. As a result, these findings are of great value for providing us more insight and understanding into the impact of financial sentiment words in financial reports.
Proceedings of the 6th International Joint Conference on Natural Language Processing (IJCNLP '13), 802-808, 2013