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Detection of Valid Sentiment-Target Pairs in Online Product Reviews and News Media Coverage

Vakulenko, Svitlana and Weichselbraun, Albert and Scharl, Arno (2016) Detection of Valid Sentiment-Target Pairs in Online Product Reviews and News Media Coverage. In: 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI-2016).

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This paper investigates the linking of sentiments to their respective targets, a sub-task of fine-grained sentiment analysis. Many different features have been proposed for this task, but often without a formal evaluation. We employ a recursive feature elimination approach to identify those features that optimize predictive performance. Our experimental evaluation draws upon two gold-standard datasets of product reviews and news articles annotated with sentiments and their targets. We introduce competitive baselines, outline the performance of the proposed approach, and report the most useful features for sentiment target linking. The results help to better understand how sentiment-target relations are expressed in the syntactic structure of natural language, and how this information can be used to build systems for fine-grained sentiment analysis.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:opinion target, fine-grained, sentiment analysis
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
ID Code:100
Deposited By: Dr Albert Weichselbraun
Deposited On:25 Oct 2016 06:43
Last Modified:25 Oct 2016 06:43

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