labeled with emoticons by Twitter users, which is easily available, meaning that no extra labeling effort is necessary. The main idea here is that such transfer of knowledge could provide additional (unsupervised) information to our method helping to improve it even further.

Related Work
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There are currently two distinct categories of sentiment analysis methods used in the social media domain: lexicon-based and those based on machine learning techniques. Machine learning methods comprise supervised classifiers trained with labeled datasets in which classes correspond to polarities (e.g. positive, negative or neutral) [@pang2002thumbs].One major challenge in this scenario is the difficulty in obtaining annotated data to train (supervised) methods due to issues such as cost and the inherent complexity of the labeling task. Accordingly, in here, we propose an unsupervised solution to deal with this sentiment analysis task.

Lexicon-based methods exploit lexical dictionaries, that is, word lists associated with sentiments or other specific features, which are usually not based on supervised learning. some challenges with lexicon-based solutions including the construction of the lexicon itself (which is usually manually done) and difficulties in adapting for domains different from which they were originally designed.

Such issues naturally call for a combination of solutions that 