Sentiment spreading: an epidemic model for lexicon-based sentiment analysis on Twitter

Pollacci L., Sirbu A., Giannotti F., Pedreschi D., Lucchese C., Muntean C. I.

semantic context  Sentiment analysis  semantic  epidemic spreading  Settore INF/01 - Informatica  sentiment classification  data mining  Lexicon  large amounts of data  Twitter  sentiment analysi  Epidemic model  short message  social networking (online)  epidemic modeling  Information diffusion  Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni  online platform  Artificial intelligence 

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.

Source: AI*IA Conference of the Italian Association for Artificial Intelligence, pp. 114–127, Bari, Italy, 14-17 November 2017

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