Phrase-level polarity disambiguation recently became attractive. Nowadays, most corners of the sentiment analysis research have been investigated while the core and hard parts are not yet intensively explored, among which polarity disambiguation is one. In this research, we propose the integration between two SentiWordNet-based resources on phrase-level polarity disambiguation: (1) its score for the best word sense identified by a word sense disambiguation algorithm and (2) its examples having the non-zero scores of the prior polarities for the related subjectivity lexicon words. The integration of subjectivity lexicon word prior polarities together with Senti-WordNet scores and examples of word contexts were presented as classifier features to reduce the classification disambiguation. Our empirical evaluation is twofold. First, an experimental analysis was intrinsically conducted using various lexical resource settings coupled with feature selection algorithms to improve the classifier contextual subjective and sentiment polarity recognition. Second, the integration was extrinsically applied to improve the opinion question answering ranking task. These evaluations prove the superiority of the proposed integration in comparison with the state-of-the-art seminal work and baselines.