Autores
Gelbukh Alexander
Título Concept-Level Sentiment Analysis with Dependency-Based Semantic Parsing: A Novel Approach.
Tipo Revista
Sub-tipo JCR
Descripción Cognitive Computation
Resumen Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In the frame of biologically inspired machine learning approaches, finding good feature sets is particularly challenging yet very important. In this paper, we focus on this fundamental issue of the sentiment analysis task. Specifically,we employ concepts as features and present a concept extraction algorithm based on a novel concept parser scheme to extract semantic features that exploit semantic relationships betweenwords in natural language text. Additional conceptual information of a concept is obtained using the ConceptNet ontology: Concepts extracted from text are sent as queries toConceptNet to extract their semantics.We select important concepts and eliminate redundant concepts using the Minimum Redundancy and Maximum Relevance feature selection technique.All selected concepts are then used to build a machine learning model that classifies a given document as positive or negative. We evaluate our concept extraction approach using a benchmark movie review dataset provided byCornellUniversity and product reviewdatasets on books, DVDs, and electronics. Comparative experimental results show that our proposed approach to sentiment analysis outperforms existing state-of-the-art methods.
Observaciones DOI 10.1007/s12559-014-9316-6
Lugar
País Alemania
No. de páginas 487–499
Vol. / Cap. Vol. 7, No. 4
Inicio 2015-01-20
Fin
ISBN/ISSN