Research on Word Sentimental Classification Based on Transductive Learning

At present, word sentimental orientation identification researches mainly fall into two categories: Machine learning and semantic comprehension, machine learning seems to work in specific-field words, but cannot handle general-field words effectively, and semantic comprehension also cannot get ideal scores at precision and recall, therefore, we put forward a fusion of transductive learning and semantic comprehension for determining words' sentimental orientation. In this paper, we firstly modify Hownet knowledge, on the basis of exist primitives, the fifth primitive -- "sentimental primitive" was proposed, and integrate it into Hownet manually, secondly, we propose a new word sentimental similarity calculation method to compute words' sentimental value, at last, combine this way with transductive learning for judgment words' sentimental orientation. The performance of experiments show that, compared with SVM and traditional semantic comprehension, it can get better results.