Istituto di Scienza e Tecnologie dell'Informazione     
Esuli A., Sebastiani F. Determining the semantic orientation of terms through gloss classification. In: CIKM'05. Proceedings of the 14th ACM Conference on Information (Bremen, DE, October 31 - November 5, 2005). Proceedings, pp. 617 - 624. Abdur Chowdhury, Norbert Fuhr, Marc Ronthaler, Hans-Jörg Shek, & (eds.). ACM press, 2005.
Sentiment classification is a recent subdiscipline of text classification which is concerned not with the topic a document is about, but with the opinion it expresses. It has a rich set of applications, ranging from tracking users' opinions about products or about political candidates as expressed in online forums, to customer relationship management. Functional to the extraction of opinions from text is the determination of the orientation of 'subjective' terms contained in text, i.e. the determination of whether a term that carries opinionated content has a positive or a negative connotation. In this paper we present a new method for determining the orientation of subjective terms. The method is based on the quantitative analysis of the glosses of such terms, i.e. the definitions that these terms are given in on-line dictionaries, and on the use of the resulting term representations for semi-supervised term classification. The method we present outperforms all known methods when tested on the recognized standard benchmarks for this task.
Subject Opinion Mining
Text Classification
Semantic Orientation
Sentiment Classification
Polarity Detection
H.3.3 Information Search and Retrieval. Information filtering
H.3.3 Information Search and Retrieval. Search process
H.3.1 Content Analysis and Indexing. Linguistic processing
I.2.7 Natural Language Processing. Text analysis
I.5.2 Design Methodology. Classifier design and evaluation

Icona documento 1) Download Document PDF

Icona documento Open access Icona documento Restricted Icona documento Private


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional