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Fine-Grained Sentiment Analysis with Structural Features
Zirn, Cäcilia
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Niepert, Mathias
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Stuckenschmidt, Heiner
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Strube, Michael
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URL:
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http://www.aclweb.org/anthology/I11-1038
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Weitere URL:
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https://www.seas.upenn.edu/~cis630/zirn_2011.pdf
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Dokumenttyp:
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Konferenzveröffentlichung
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Erscheinungsjahr:
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2011
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Buchtitel:
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Proceedings of the 5th International Joint Conference on Natural Language Processing
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Titel einer Zeitschrift oder einer Reihe:
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Proceedings of the 5th International Joint Conference on Natural Language Processing
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Seitenbereich:
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336-344
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Veranstaltungstitel:
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IJCNLP 2011
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Veranstaltungsort:
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Chiang Mai, Thailand
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Veranstaltungsdatum:
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8-13 November 2011
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Ort der Veröffentlichung:
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Chiang Mai
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Verlag:
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Asian Federation of Natural Language Processing
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Verwandte URLs:
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
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Fachgebiet:
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004 Informatik
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Freie Schlagwörter (Englisch):
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Sentiment Analysis, Opinion Mining
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Abstract:
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Sentiment analysis is the problem of determining the polarity of a text with respect to a particular topic. For most applications, however, it is not only necessary to derive the polarity of a text as a whole but also to extract negative and positive utterances on a more fine-grained level. Sentiment analysis systems working on the (sub-)sentence level, however, are difficult to develop since shorter textual segments rarely carry enough information to determine their polarity out of context. In this paper, therefore, we present a fully automatic framework for fine-grained sentiment analysis on the subsentence level combining multiple sentiment lexicons and neighborhood as well as discourse relations to overcome this problem. We use Markov logic to integrate polarity scores from different sentiment lexicons with information about relations between neighboring segments, and evaluate the approach on product reviews. The experiments show that the use of structural features improves the accuracy of polarity predictions achieving accuracy scores of up to 69%.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Suche Autoren in
BASE:
Zirn, Cäcilia
;
Niepert, Mathias
;
Stuckenschmidt, Heiner
;
Strube, Michael
Google Scholar:
Zirn, Cäcilia
;
Niepert, Mathias
;
Stuckenschmidt, Heiner
;
Strube, Michael
ORCID:
Zirn, Cäcilia, Niepert, Mathias, Stuckenschmidt, Heiner  ORCID: https://orcid.org/0000-0002-0209-3859 and Strube, Michael
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