Learning Sentimental Weights of Mixed gram Terms for Classification and Visualization
Sentimental analysis is an important topic in natural language processing and opinion mining. Many previous studies have reported to judge whether a term is with emotion or not. However, little work has been done in measuring degrees of sentiment for these terms. For example, the word excellent has stronger positive sentiment than the word good and okay. In this paper, we investigate how to model this intricate sentimental difference by assigning sentimental weights. A simple and effective model is proposed based on logistic regression to extract emotional terms associated with sentiment weights. Weighted terms can be used in sentiment classification and visualization by drawing emotional clouds of texts. The new model is tested using uni gram, bi gram and mixed gram language models on two benchmark datasets. The empirical results show that the new model is highly efficient with comparable accuracy to other sentiment classifiers.
PRICAI