Sentilo: A Semantic Web Based Core Engine to Efficiently Perform Sentiment Analysis. In this paper we present a domain-independent framework that creates a sentiment analysis model by mixing Semantic Web technologies with natural language processing approaches (This work is supported by the project PRISMA SMART CITIES, funded by the Italian Ministry of Research and Education under the program PON.). Our system, called Sentilo, provides a core sentiment analysis engine which fully exploits semantics. It identifies the holder of an opinion, topics and sub-topics the opinion is referred to, and assesses the opinion trigger. Sentilo uses an OWL opinion ontology to represent all this information with an RDF graph where holders and topics are resolved on Linked Data. Anyone can plug its own opinion scoring algorithm to compute scores of opinion expressing words and come up with a combined scoring algorithm for each identified entities and the overall sentence.

References in zbMATH (referenced in 3 articles )

Showing results 1 to 3 of 3.
Sorted by year (citations)

  1. Zhang, Yazhou; Song, Dawei; Zhang, Peng; Wang, Panpan; Li, Jingfei; Li, Xiang; Wang, Benyou: A quantum-inspired multimodal sentiment analysis framework (2018)
  2. Athanasiou, Vasileios; Maragoudakis, Manolis: A novel, gradient boosting framework for sentiment analysis in languages where NLP resources are not plentiful: a case study for modern Greek (2017)
  3. Recupero, Diego; Consoli, Sergio; Gangemi, Aldo; Nuzzolese, Andrea; Spampinato, Daria: A semantic web based core engine to efficiently perform sentiment analysis (2014) ioport