Authors:
Manuela Angioni
and
Franco Tuveri
Affiliation:
CRS4, Center of Advanced Studies and Research and Development in Sardinia, Italy
Keyword(s):
Sentiment analysis, Opinion mining, NLP, Text categorization, Semantic disambiguation.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Knowledge Acquisition
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Symbolic Systems
Abstract:
Understanding the meaning of a text depends on the knowledge the reader has about the topic addressed in a document, starting from the most complex concept to the simplest one. The representation of the knowledge is generally performed by ontologies, semantic networks, or typified by statistical algorithms able to organize the contents according to rules based on frequency of terms or synsets. The Opinion Mining is a way to go beyond text categorization through the analysis of the opinions related to a specific topic: a product, a service, a tourist location, etc. In this paper we propose to apply our experience in the semantic analysis of textual resources to the Opinion Mining task, with the aim to propose a different approach to the extraction of feature terms, performing a contextualisation by means of semantic categorisation, a semantic net of concept and by a set of qualities associated to the sense expressed by adjectives and adverbs.