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Sentiment Spreading: An Epidemic Model for Lexicon-Based Sentiment Analysis on Twitter

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10640)


While 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.

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    The Semeval 2013 Message Polarity Classification competition (task B),

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    The Semeval 2014 Message Polarity Classification competition (task B),

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    The Earth Hour 2015 corpus:


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This work has been funded by the European project SoBigData Research Infrastructure - Big Data and Social Mining Ecosystem under the INFRAIA-H2020 program (grant agreement 654024).

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Correspondence to Laura Pollacci .

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Pollacci, L., Sîrbu, A., Giannotti, F., Pedreschi, D., Lucchese, C., Muntean, C.I. (2017). Sentiment Spreading: An Epidemic Model for Lexicon-Based Sentiment Analysis on Twitter. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F. (eds) AI*IA 2017 Advances in Artificial Intelligence. AI*IA 2017. Lecture Notes in Computer Science(), vol 10640. Springer, Cham.

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