Simplified hybrid approach for detection of semantic orientations in economic texts

Abstract

The aim of this work is to reproduce the approach to detecting semantic orientations in economic texts that was presented in the paper Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts by Malo et al. The approach employs the Linearized Phrase Structure model for sentence level classification of short economic texts into a positive, negative or neutral category from investor’s perspective and yields state-of-the-art results. The proposed method employs both rule based linguistic models and machine learning. Where possible we follow the same approach as described in the original paper, with some documented modifications. Our solution is simplified in at least two aspects, but its performance is comparable to the original and overall remains better than the reported results of other benchmark algorithms mentioned in the original paper. The differences between the two models and results are described in detail and lead to conclusion that the original approach is to a large extent repeatable and that our simplified version does not overly sacrifice performance for generalizability.