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Istituto di Scienza e Tecnologie dell'Informazione     
Esuli A., Sebastiani F. Determining term subjectivity and term orientation for opinion mining. In: EACL'06, the 11th Conference of the European Association of Computational Linguistics (Trento, Italy, 3-7 April 2006). Proceedings, vol. 1 pp. 193 - 200. Diana McCarthy and Shuly Wintner (eds.). Association for Computational Linguistics, 2006.
 
 
Abstract
(English)
Opinion mining is a recent subdiscipline of information retrieval which is concerned not with the topic a document is about, but with the opinion it expresses. To aid the extraction of opinions from text, recent work has tackled the issue of determining the orientation of 'subjective' terms contained in text, i.e. deciding whether a term that carries opinionated content has a positive or a negative connotation; this is believed to be of key importance for identifying the orientation of documents, i.e. determining whether a document expresses a positive or negative opinion about its subject matter We contend that the plain determination of the orientation of terms is not a realistic problem, since it starts from the non-realistic assumption that we already know whether a term is subjective or not; this would imply that a linguistic resource that marks terms as 'subjective' or 'objective' is available, which is usually not the case. In this paper we confront the task of deciding whether a given term has a positive connotation, or a negative connotation, or has no subjective connotation at all; this problem thus subsumes the problem of determining subjectivity/objectivity and the problem of determining orientation. We tackle this problem by testing three different variants of the semi-supervised method for orientation detection. Our results show that determining subjectivity and orientation is a much harder problem than determining orientation alone.
Subject Text Classification
Opinion Mining
Sentiment Classification
Semantic Orientation
Polarity Detection
Subjectivity 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


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