The huge variability of trends, community interests and jargon is a crucial challenge for the application of language technologies to Social Media analysis. Models, such as grammars and lexicons, are exposed to rapid obsolescence, due to the speed at which topics as well as slogans change during time. In Sentiment Analysis, several works dynamically acquire the so-called opinionated lexicons. These are dictionaries where information regarding subjectivity aspects of individual words are described. This paper proposes an architecture for dynamic sentiment analysis over Twitter, combining structured learning and lexicon acquisition. Evidence about the beneficial effects of a dynamic architecture is reported through large scale tests over Twitter streams in Italian.

Castellucci, G., Croce, D., De Cao, D., & Basili, R. (2016). User mood tracking for opinion analysis on Twitter. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.76-88). Springer Verlag [10.1007/978-3-319-49130-1_7].

User mood tracking for opinion analysis on Twitter

CASTELLUCCI, GIUSEPPE;CROCE, DANILO;DE CAO, DIEGO;BASILI, ROBERTO
2016-01-01

Abstract

The huge variability of trends, community interests and jargon is a crucial challenge for the application of language technologies to Social Media analysis. Models, such as grammars and lexicons, are exposed to rapid obsolescence, due to the speed at which topics as well as slogans change during time. In Sentiment Analysis, several works dynamically acquire the so-called opinionated lexicons. These are dictionaries where information regarding subjectivity aspects of individual words are described. This paper proposes an architecture for dynamic sentiment analysis over Twitter, combining structured learning and lexicon acquisition. Evidence about the beneficial effects of a dynamic architecture is reported through large scale tests over Twitter streams in Italian.
15th International Conference on Italian Association for Artificial Intelligence, AIIA 2016
ita
2016
Bioengineering
Rilevanza nazionale
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
Settore INF/01 - Informatica
English
Opinion mining; Polarity lexicons; Sentiment analysis; Social media analytics; Theoretical Computer Science; Computer Science (all)
http://springerlink.com/content/0302-9743/copyright/2005/
Intervento a convegno
Castellucci, G., Croce, D., De Cao, D., & Basili, R. (2016). User mood tracking for opinion analysis on Twitter. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.76-88). Springer Verlag [10.1007/978-3-319-49130-1_7].
Castellucci, G; Croce, D; DE CAO, D; Basili, R
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2108/189346
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