part of our approach – thus, we focus on the ones evaluated by [@ribeiro2015benchmark] as it provides the most recent and complete sentence-level benchmark of off-the-shelf sentiment analysis methods.

There are few small adaptations on some methods to provide as output positive, negative and neutral decisions. For this, we have used the codes shared by the authors of [@ribeiro2015benchmark]. More details about these implementations can be found there. The considered methods include: VADER [@hutto2014vader], AFINN[@nielsen2011new], OpinionLexicon [@Hu:2004:MSC:1014052.1014073], Umigon [@levallois2013umigon], SO-CAL [@taboada2011lexicon], Pattern.en [@smedt2012pattern], Sentiment140 [@mohammad2013nrc], EmoLex [@journals/ci/MohammadT13], Opinion Finder[@wilson2005opinionfinder], and SentiStrength [@thelwall2013heart]. A brief description of these methods can also in found in [@ribeiro2015benchmark].

We also note that all methods exploit light-weight unsupervised approaches that rely on lexical dictionaries, usually implemented as a hash-like data structure. For this reason, the execution performance of our combined, as well as the individual methods, does not require any powerful hardware platform.

0

[|l|l|]{} **Method** & **Description**\
VADER [@hutto2014vader] &

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   This method is based on a lexicon dictionary. Valence Aware Dictionary for sEntiment Reasoning (VADER) is a human-validated sentiment analysis method developed for micro-blogging and social media, requiring no training data.
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  : Description of individual methods combined in our approach[]{data-label="table:methods"}

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AFINN 