In recent years, the Web and social media are growing exponentially. We are provided with documents which have opinions expressed about several topics. This constitute a rich source for Natural Language Processing tasks, in particular, Sentiment Analysis. In this work, we aim at constructing a sentiment dictionary based on words obtained from web pages related to a specific domain. To do so, we correlate candidate opinion words, seed words and domain using AcroDefMI3 and TrueSkill methods. This dictionarybased approach is compared to the SentiWordNet lexical resource. Experimental results show suitability of our approach for multiple domains and infrequent opinion words.