[@nielsen2011new] &

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   Builds a Twitter based sentiment Lexicon including Internet slangs and obscene words. AFINN uses a dictionary created to provide emotional ratings for English words.
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  : Description of individual methods combined in our approach[]{data-label="table:methods"}

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OpinionLexicon [@Hu:2004:MSC:1014052.1014073] &

[ p[8cm]{}]{}  Also known as Sentiment Lexicon, it is a lexical-based method consisting of two lists with 2,006 positive words and 4,783 negative words. It includes slang, misspellings, morphological variants, and social-media markups.

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Umigon [@levallois2013umigon] &

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   Disambiguate tweets using lexicon with heuristics to detect negations plus elongated words and hashtags evaluation.
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  : Description of individual methods combined in our approach[]{data-label="table:methods"}

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SO-CAL [@taboada2011lexicon] &

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   Creates a new Lexicon with unigrams (verbs, adverbs, nouns and adjectives) and multi-grams (phrasal verbs and intensifiers) hand ranked with scale +5 (strongly positive) to -5 (strongly negative). Authors also included part of speech processing, negation and intensifiers.
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  : Description of individual methods combined in our approach[]{data-label="table:methods"}

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Pattern.en [@smedt2012pattern] &

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   Python programming package (toolkit) that deal with NLP, Web Mining and Sentiment Analysis. Sentiment analysis is provided through averaging scores from words in the sentence according to a bundle lexicon of adjectives.
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  : Description of individual methods 