Neural sentiment analysis for a real-world application

Daniele Bonadiman, Giuseppe Castellucci, Andrea Favalli, Raniero Romagnoli, Alessandro Moschitti

Research output: Contribution to journalConference article


In this paper, we describe our neural network models for a commercial application on sentiment analysis. Different from academic work, which is oriented towards complex networks for achieving a marginal improvement, real scenarios require flexible and efficient neural models. The possibility to use the same models on different domains and languages plays an important role in the selection of the most appropriate architecture. We found that a small modification of the state-of-the-art network according to academic benchmarks led to a flexible neural model that also preserves high accuracy.

Original languageEnglish
JournalCEUR Workshop Proceedings
Publication statusPublished - 1 Jan 2017


ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Bonadiman, D., Castellucci, G., Favalli, A., Romagnoli, R., & Moschitti, A. (2017). Neural sentiment analysis for a real-world application. CEUR Workshop Proceedings, 2006.