DISSERTATION ABSTRACT

Automatic Generation of Lexical Resources for Opinion
Mining: Models, Algorithms and Applications
Andrea Esuli∗
Istituto di Scienza e Tecnologie dell’Informazione
Consiglio Nazionale delle Ricerche
via G. Moruzzi, 156124, Pisa - ITALY
andrea.esuli@isti.cnr.it
Opinion mining is a recent discipline at the crossroads of Information Retrieval and of Computational Linguistics which is concerned not with the topic a document is about, but with theopinion 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 relationshipmanagement.
Functional to the extraction of opinions from text is the determination of the relevant entities ofthe language that are used to express opinions, and their opinion-related properties. For example,
determining that the term beautiful casts a positive connotation to its subject.
In this thesis we investigate on the automatic recognition of opinion-related properties of terms.
This results into building opinion-related lexical resources, which can be used into opinion mining
applications.
We start from the (relatively) simple problem of determining the orientation of subjectiveterms. We propose an original semi-supervised term classification model [2] that is based on thequantitative analysis of the glosses of such terms, i.e. the definitions that these terms are given in
on-line dictionaries. This method outperforms all known methods when tested on the recognized
standard benchmarks for this task.
We show how our method is capable to produce good results on more complex tasks, suchas discriminating subjective terms (e.g., good) from objective ones (e.g., green) [3], or classifying
terms on a fine-grained attitude taxonomy [1].
We then propose a relevant refinement of the task, i.e., distinguishing the opinion-related
properties of distinct term senses. We present SentiWordNet [4], a novel high-quality, highcoverage lexical resource, where each one of the 115,424 senses contained in WordNet has been
automatically evaluated on the three dimensions of positivity, negativity, and objectivity.
We propose also an original and effective use of random-walk models to rank term senses bytheir positivity or negativity [5, 6]. The random-walk algorithms we present have a great application potential also outside the opinion mining area, for example in word sense disambiguation
tasks. A result of this experience is the generation of an improved version of SentiWordNet.
∗Student of the Doctorate in Information Engineering of the University of Pisa

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We finally evaluate and compare the various versions of SentiWordNet we present herewith other opinion-related lexical resources well-known in literature, experimenting their use in an
Opinion Extraction application. We show that the use of SentiWordNet produces a significant
improvement with respect to the baseline system, not using any specialized lexical resource, andalso with respect to the use of other opinion-related lexical resources.
Available online: http://etd.adm.unipi.it/theses/available/etd-04042008-163914/

References[1] S. Argamon, B. Kenneth, A. Esuli, and F. Sebastiani. Automatically determining attitude typeand force for sentiment analysis. In Proceedings of LTC-07, the 3rd Language & TechnologyConference, Poznan, PL, October 2007.
[2] A. Esuli and F. Sebastiani. Determining the semantic orientation of terms through gloss
classification. In Proceedings of CIKM-05, the ACM SIGIR Conference on Information andKnowledge Management, pages 617–624, Bremen,DE, 2005. ACM Press.
[3] A. Esuli and F. Sebastiani. Determining term subjectivity and term orientation for opinion mining. In Proceedings EACL-06, the 11rd Conference of the European Chapter of theAssociation for Computational Linguistics, pages 193–200, Trento, IT, 2006.
[4] A. Esuli and F. Sebastiani. SentiWordNet: A publicly available lexical resource for opinion
mining. In Proceedings of LREC-06, the 5th Conference on Language Resources and Evaluation, Genova, IT, 2006.
[5] A. Esuli and F. Sebastiani. Pageranking wordnet synsets: An application to opinion mining. In Proceedings of ACL-07, the 45th Annual Meeting of the Association of Computational
Linguistics, pages 424–431, Prague, CZ, June 2007. Association for Computational Linguistics.
[6] A. Esuli and F. Sebastiani. Random-walk models of term semantics: An application to opinionrelated properties. In Proceedings of LTC-07, the 3rd Language & Technology Conference,Poznan, PL, October 2007.

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Vol. 42 No. 2 December 2008