Generating and mixing feature sets from language models for sentiment classification

Yoonjae Jeong, Youngho Kim, Seongchan Kim, Sung-Hyon Myaeng, Hyo-Jung Oh
2009 2009 International Conference on Natural Language Processing and Knowledge Engineering  
This paper presents methods for mixing feature sets in sentence-level sentiment analysis where a sentence is classified into one of three classes: positive, negative, and neutral. Motivated by the need to classify sentences in Korean whose sentiment-revealing expressions tend to have different effects according to their syntactic categories, we employed a language modeling (LM) approach with 162 different LMs based on syntactic categories that are effectively combined with a Logistic Regression
more » ... classifier. The experimental results show that this approach significantly outperforms clue-based SVM classifiers. The enumeration of feature types arising from the LMs for the Logistic Regression classifier allowed us to show that domain specific models can be smoothed with a general model and that attaching a syntactic category to a feature helps improving effectiveness. The classification results are further improved by applying a clue-based classifier. The rationale behind this two-step process is to classify sentences with a relatively conservative classifier in picking positive and negative sentences and to apply a high-precision classifier to the sentences in the neutral class.
doi:10.1109/nlpke.2009.5313746 dblp:conf/nlpke/JeongKKMO09 fatcat:fzajrft5k5gwbi44pdubjmxnqu