Incorporating Feature-based and Similarity-based Opinion Mining - CTL in NTCIR-8 MOAT

This paper presents the design and implementation of an opinion mining system developed by NLPCity group for NTCIR-8 MOAT evaluation, named CTL-OM. CTL-OM incorporates two opinion mining approach, namely feature-based approach and similaritybased approach. The feature-based approach incorporates computational features at punctuation-, word-, collocation-, phrase-, sentence-, paragraphand document-level in a coarsefine multi-pass classification framework. The opinion holders and opinions targets in the opinionated sentences are then recognized. The similarity-based approach works in a different way. This approach estimates the similarity between the example sentences and testing sentence and identifies the similar example sentencetesting sentence pair. The opinion components annotated in the example sentence are utilized to recognize the corresponding components in the testing sentence. The analysis outputs by these two approaches are integrated to obtain the final opinion mining results. CTL-OM achieved promising results in Traditional Chinese and Simplified Chinese evaluation in MOAT-8, respectively. This result shows that the incorporation of featurebased and similarity-based opinion mining approach is effective.