Many of the state-of-the-art methods for constructing a polarity lexicon rely on the propagation of polarity on the lexical network. In one of those methods, where the Ising spin model is employed as a probabilistic model, it is reported that the system exhibits the phase transition in the vicinity of the optimal temperature parameter. We provide an analysis of this phenomenon from the viewpoint of statistical mechanics and clarify the underlying mechanism. On the basis of this analysis, we propose a scheme for improving the extraction performance, i.e., by removing the largest eigenvalue component from the weight matrix. Experimental results show that the scheme significantly improves the accuracy of the extraction of the semantic orientations at negligible additional computational cost, outperforming the state-of-the-art algorithms. We also explore the origin of the high classification performance by analyzing eigenvalues of the weight matrix and a linearized model.
['Takuma Goto', 'Yoshiyuki Kabashima', 'Hiroya Takamura']
Statistical Mechanical Analysis of Semantic Orientations on Lexical Network