Show simple item record

dc.contributor.authorZhang, K.
dc.contributor.authorXie, Y.
dc.contributor.authorYang, Y.
dc.contributor.authorSun, A.
dc.contributor.authorLiu, H.
dc.contributor.authorChoudhary, .
dc.date.accessioned2015-02-02T04:15:35Z
dc.date.available2015-02-02T04:15:35Z
dc.date.issued2014-10
dc.identifier.bibliographicCitationZhang, K. P., Xie, Y. S., Yang, Y., Sun, A. R., Liu, H. C. and Choudhary, A. Incorporating conditional random fields and active learning to improve sentiment identification. Neural Networks. 2014. 58: 60-67. 10.1016/j.neunet.2014.04.005.en_US
dc.identifier.issn8805018
dc.identifier.issn8805018
dc.identifier.urihttp://hdl.handle.net/10027/19316
dc.descriptionNOTICE: This is the author’s version of a work that was accepted for publication in Neural Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published Neural Networks, DOI: 10.1016/j.neunet.2014.04.005en_US
dc.description.abstractMany machine learning, statistical, and computational linguistic methods have been developed to identify sentiment of sentences in documents, yielding promising results. However, most of state-of-the-art methods focus on individual sentences and ignore the impact of context on the meaning of a sentence. In this paper, we propose a method based on conditional random fields to incorporate sentence structure and context information in addition to syntactic information for improving sentiment identification. We also investigate how human interaction affects the accuracy of sentiment labeling using limited training data. We propose and evaluate two different active learning strategies for labeling sentiment data. Our experiments with the proposed approach demonstrate a 5%-15% improvement in accuracy on Amazon customer reviews compared to existing supervised learning and rule-based methods.en_US
dc.publisherNeural Networksen_US
dc.subjectActive learningen_US
dc.subjectConditional random fieldsen_US
dc.subjectCustomer reviewsen_US
dc.subjectSentiment analysisen_US
dc.titleIncorporating conditional random fields and active learning to improve sentiment identification.en_US
dc.typeArticleen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record