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Sentiment embedded semantic space for more accurate sentiment analysis
conference contribution
posted on 2018-01-01, 00:00 authored by J Jiang, Y Lu, M Yu, Gang LiGang Li, C Liu, W Huang, F Zhang© 2018, Springer Nature Switzerland AG. Word embedding is one common word vector representation with improved performance for sentiment analysis task. Most existing methods of learning context-based word embedding are semantic oriented, but they typically fail to capture the sentiment information. This may result in words with similar vectors but with very different sentiment polarities, thus degrading the followed sentiment analysis performance. In this paper, we propose a novel and efficient method to yield the Sentiment Embedded Semantic Space that captures the connection between the sentiment space and the semantic space. The proposed method is based on K-means and CNN. In addition, we develop a more fine-grained sentiment dictionary based on HowNet Dictionary together with the processing dataset. Extensive experiments on benchmark datasets show that the proposed method leads to more accurate sentiment classifier and reduces the task-specific word embedding effort.
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Event
Knowledge Science, Engineering and Management. Conference (2018 : Changchun, China)Volume
11062Series
Lecture Notes in Computer SciencePagination
221 - 231Publisher
SpringerLocation
Changchun, ChinaPlace of publication
Berlin, GermanyPublisher DOI
Start date
2018-08-17End date
2018-08-19ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319992464Language
engPublication classification
E1 Full written paper - refereedEditor/Contributor(s)
W Liu, F Giunchiglia, B YangTitle of proceedings
KSEM 2018 : Knowledge Science, Engineering and ManagementUsage metrics
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