Using morphological and syntactic structures for Chinese opinion analysis
Journal
2009 Conference on Empirical Methods in Natural Language Processing
Pages
1260-1269
Date Issued
2009
Author(s)
Abstract
This paper employs morphological structures and relations between sentence segments for opinion analysis on words and sentences. Chinese words are classified into eight morphological types by two proposed classifiers, CRF classifier and SVM classifier. Experiments show that the injection of morphological information improves the performance of the word polarity detection. To utilize syntactic structures, we annotate structural trios to represent relations between sentence segments. Experiments show that considering structural trios is useful for sentence opinion analysis. The best f-score achieves 0.77 for opinion word extraction, 0.62 for opinion word polarity detection, 0.80 for opinion sentence extraction, and 0.54 for opinion sentence polarity detection. ? 2009 ACL and AFNLP.
Type
conference paper