Fine-Grained Sentiment Analysis with Structural Features


Zirn, Cäcilia ; Niepert, Mathias ; Stuckenschmidt, Heiner ; Strube, Michael



URL: http://www.aclweb.org/anthology/I11-1038
Additional URL: https://www.seas.upenn.edu/~cis630/zirn_2011.pdf
Document Type: Conference or workshop publication
Year of publication: 2011
Book title: Proceedings of the 5th International Joint Conference on Natural Language Processing
Page range: 336-344
Conference title: IJCNLP 2011
Location of the conference venue: Chiang Mai, Thailand
Date of the conference: 8-13 November 2011
Place of publication: Chiang Mai
Publishing house: Asian Federation of Natural Language Processing
Related URLs: https://www.semanticscholar.org/paper/Fine-Grained-Sentiment-Analysis-with-Structural-Zirn-Niepert/d4019b7d8c20203e24a4252d845d15c4072b86a9
Publication language: English
Institution: Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Keywords (English): Sentiment Analysis, Opinion Mining
Abstract: 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%.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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Zirn, Cäcilia ; Niepert, Mathias ; Stuckenschmidt, Heiner ORCID: 0000-0002-0209-3859 ; Strube, Michael Fine-Grained Sentiment Analysis with Structural Features. 336-344 In: Proceedings of the 5th International Joint Conference on Natural Language Processing (2011) Chiang Mai IJCNLP 2011 (Chiang Mai, Thailand) [Conference or workshop publication]


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