the performance of the base methods varies significantly. As we shall see, our solution produced the most consistent results across all datasets and contexts.

Combining Methods {#sec:methods}
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Sentiment analysis can be applied to different tasks. We restrict our focus on combining those efforts related to detect the polarity (i.e. positivity, negativity, neutrality) of a given short text (i.e. sentence-level). In other words, given a set $S$ of opinionated sentences, we want to determine whether each sentence $s$ $in$ $S$ expresses a positive, negative or neutral opinion. We focus our effort on combining only unsupervised “off-the-shelf” methods. Our strategy consists of using the output label predicted by each individual method as input for a bootstrapping technique – a self-starting process supposed to proceed without external input. Next we present the proposed technique.

Our **bootstrapping technique** is an unsupervised machine learning algorithm that uses the sentiment scores produced by each individual sentiment analysis method to create a training set for a supervised machine learning algorithm. With this algorithm, we are able to produce a final result regarding the sentiment of a sentence. Note that, we did not need to use any manually labeled data in order to produce the model.

We 