As it is known, the success of a newspaper article for the public opinion can be measured by the degree in which the journalist is able to report and modify (if needed) attitudes, opinions, feelings and political beliefs. We present a symbolic system for Italian, derived from GETARUNS, which integrates a range of natural language processing tools with the intent to characterise the print press discourse. The system is multilingual and can produce deep text understanding. This has been done on some 500K words of text, extracted from three Italian newspaper in order to characterize their stance on a deep political crisis situation. We tried two different approaches: a lexicon-based approach for semantic polarity using off-the-shelf dictionaries with the addition of manually supervised domain related concepts; another one is a feature-based semantic and pragmatic approach, which computes propositional level analysis with the intent to better characterize important component like factuality and subjectivity. Results are quite revealing and confirm the otherwise common knowledge about the political stance of each newspaper on such topic as the change of government, that took placeatthe end of lastyear,2011.
|Titolo:||Opinion and Sentiment Analysis of Italian print press|
|Autori interni:||DELMONTE, Rodolfo|
|Data di pubblicazione:||2013|
|Rivista:||INTERNATIONAL JOURNAL OF ADVANCED COMPUTER AND COMMUNICATION TECHNOLOGY|
|Appare nelle tipologie:||2.1 Articolo su rivista |