This study proposes an experimental approach to building a domain-dependent sentiment lexicon, a crucial resource for sentiment analysis of Chinese texts, in the Deco Sentiment Analysis platform developed at the Digital Language and Knowledge Contents Research Association (DICORA) research center. More than 150,000 hotel reviews were used for training with word2vec models, and 80 emotional words were first selected as seed words. TF-IDF was used to measure the relevance of the sentiment vocabulary in hotel reviews. In order to build the feature vector representation of each candidate word, the similarities between a term and the other 80 seed words were calculated. Then, expanding the lexicon involved a double propagation method that requires recognizing feature words related to the sentiment expressions. Through the bootstrap of sentiment terms and features, the expansion of the sentiment lexicon could be performed. The evaluation of the expanded result is confirmed with rates set at 77.4% of precision and 92.6% of recall performance.