To improve the performance of sentiment classification for word of mouth (WOM), this research re-evaluates objective sentiment words in a sentiment lexicon, SentiWordNet. SentiWordNet provides each synonymous set with three sentiment values regarding positivity, negativity, and objectivity. As the evaluation of sentiments of words in WOM is useful for sentiment classification, SentiWordNet has become a public and popular lexicon resource. This sentiment lexicon includes 117,659 entries but 93.75% of them have a stronger objective sentiment tendency than their sentimental counterparts. A sentiment lexicon such as SentiWordNet with so many objective words may suffer from noise in the application of sentiment classification. This research revises sentiment value and tendency for objective words in SentiWordNet based on assessment of the co-relevance of each objective word and its associated sentiment sentences. Experiments show that this proposed approach is significantly better than the traditional non-revised approach as evaluated by the classification accuracy criterion.