View : 55 Download: 0

Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean

Title
Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean
Authors
Song M.Park H.Shin K.-S.
Ewha Authors
신경식박현정
SCOPUS Author ID
신경식scopus; 박현정scopus
Issue Date
2019
Journal Title
Information Processing and Management
ISSN
0306-4573JCR Link
Citation
Information Processing and Management vol. 56, no. 3, pp. 637 - 653
Keywords
Attention mechanismEmbedding learningLSTMSentiment analysis
Publisher
Elsevier Ltd
Indexed
SCIE; SSCI; SCOPUS scopus
Document Type
Article
Abstract
Although deep learning breakthroughs in NLP are based on learning distributed word representations by neural language models, these methods suffer from a classic drawback of unsupervised learning techniques. Furthermore, the performance of general-word embedding has been shown to be heavily task-dependent. To tackle this issue, recent researches have been proposed to learn the sentiment-enhanced word vectors for sentiment analysis. However, the common limitation of these approaches is that they require external sentiment lexicon sources and the construction and maintenance of these resources involve a set of complexing, time-consuming, and error-prone tasks. In this regard, this paper proposes a method of sentiment lexicon embedding that better represents sentiment word's semantic relationships than existing word embedding techniques without manually-annotated sentiment corpus. The major distinguishing factor of the proposed framework was that joint encoding morphemes and their POS tags, and training only important lexical morphemes in the embedding space. To verify the effectiveness of the proposed method, we conducted experiments comparing with two baseline models. As a result, the revised embedding approach mitigated the problem of conventional context-based word embedding method and, in turn, improved the performance of sentiment classification. © 2018 Elsevier Ltd
DOI
10.1016/j.ipm.2018.12.005
Appears in Collections:
경영대학 > 경영학전공 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE