Sentiment Classification and Polarity Shifting

Polarity shifting marked by various linguistic structures has been a challenge to automatic sentiment classification. In this paper, we propose a machine learning approach to incorporate polarity shifting information into a document-level sentiment classification system. First, a feature selection method is adopted to automatically generate the training data for a binary classifier on polarity shifting detection of sentences. Then, by using the obtained binary classifier, each document in the original polarity classification training data is split into two partitions, polarity-shifted and polarity-unshifted, which are used to train two base classifiers respectively for further classifier combination. The experimental results across four different domains demonstrate the effectiveness of our approach.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Shoushan Li, Sophia Yat Mei Lee, Ying Chen, Chu-Ren Huang, Guodong Zhou
• Keywords: Binary Classifier, COLING 2010, Computational Linguistics, Sentiment Classification, Training Data