combined with a rule-based classifier based on several publicly available sentiment lexicons to extract positive and negative sentiment words. After the rule-based classifier is applied, the NB is used to classify the remaining tweets as positive or negative.

[@deriu2017leveraging] also uses a weakly supervised approach to multi-language sentiment classification task. The developed method evaluates large amounts of weakly supervised data in various languages to train a multi-layer convolutional neural network, but its focus is on multilingual sentiment classification.

Wikisent, proposed by [@mukherjee2012wikisent] also describes a weakly supervised system for sentiment analysis classification. They use text summarization focused on movie reviews domain in order to obtain knowledge about the various technical aspects of the movie. After that, the summary of the opinions are classified by using the SentiWordNet lexicon method.

To summarize, many authors proposed supervised ensemble classifiers, but differently from those, we propose a novel approach by combining a series of "state-of-the-practice” existing methods in a totally unsupervised and in much more elaborated manner exploiting bootstrapping and (unsupervised) transfer learning. Another major difference of our effort is that we evaluate using multiple labeled datasets, covering multiple domains and social media sources. This is critical for an unsupervised approach given that 