Categorizing terms´ subjectivity and polarity manually for opinion mining in Chinese

The paper proposes three principles and a clear guideline to create a large-scale Chinese sentiment lexicon for opinion mining systems. The comparative analysis between the criterions used in ready-made resource and the subjectivity and polarity in opinion mining system is also discussed in the paper. With the analysis results, we manually categorize 116533 entries of Hownet terms for the lexicon according to the guideline. Two experiments are conducted to investigate the reliability of manual subjectivity labeling of the terms. The result of the first experiment shows significant high agreement between different annotators, while the second experiment demonstrates the effectiveness of our lexicon in judging the polarity of subjective sentence considering the simple recognition method and verifying our approach´s high reliability.