Title: Semi-supervised Learning with Multi-Domain Sentiment Word Embeddings. Abstract: Word embeddings are known to boost performance of many NLP tasks such as text classification, meanwhile they can be enhanced by labels at the document level to capture nuanced meaning such as sentiment and topic. Can one combine these two research directions to benefit from both? In this paper, we propose to jointly train a text classifier with a label-enhanced and domain-aware word embedding model, using an unlabeled corpus and only a few labeled data from non-target domains. The embeddings are trained on the unlabed corpus and enhanced by pseudo labels coming from the classifier, and at the same time are used by the classifier as input and training signals. We formalize this symbiotic cycle in a variational Bayes framework, and show that our method improves both the embeddings and the text classifier, outperforming state-of-the-art domain adaptation and semi-supervised learning techniques. We conduct detailed ablative tests to reveal gains from important components of our approach. The source code and experiment data will be publicly released.