01783nas a2200169   4500008004100000245009100041210006900132260001300201520120400214100002001418700001701438700002101455700002001476700002201496700002901518856006601547       2017                        eng d00aSentiment Spreading: An Epidemic Model for Lexicon-Based Sentiment Analysis on Twitter0 aSentiment Spreading An Epidemic Model for LexiconBased Sentiment  bSpringer3 aWhile sentiment analysis has received significant attention in the last years, problems still exist when tools need to be applied to microblogging content. This because, typically, the text to be analysed consists of very short messages lacking in structure and semantic context. At the same time, the amount of text produced by online platforms is enormous. So, one needs simple, fast and effective methods in order to be able to efficiently study sentiment in these data. Lexicon-based methods, which use a predefined dictionary of terms tagged with sentiment valences to evaluate sentiment in longer sentences, can be a valid approach. Here we present a method based on epidemic spreading to automatically extend the dictionary used in lexicon-based sentiment analysis, starting from a reduced dictionary and large amounts of Twitter data. The resulting dictionary is shown to contain valences that correlate well with human-annotated sentiment, and to produce tweet sentiment classifications comparable to the original dictionary, with the advantage of being able to tag more tweets than the original. The method is easily extensible to various languages and applicable to large amounts of data.1 aPollacci, Laura1 aSirbu, Alina1 aGiannotti, Fosca1 aPedreschi, Dino1 aLucchese, Claudio1 aMuntean, Cristina, Ioana  uhttps://link.springer.com/chapter/10.1007/978-3-319-70169-1_9