Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이기천 | - |
dc.date.accessioned | 2019-12-06T00:41:33Z | - |
dc.date.available | 2019-12-06T00:41:33Z | - |
dc.date.issued | 2018-03 | - |
dc.identifier.citation | EXPERT SYSTEMS WITH APPLICATIONS, v. 94, page. 218-227 | en_US |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.issn | 1873-6793 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0957417417304979?via%3Dihub | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/117776 | - |
dc.description.abstract | With the rapid growth of social media, text mining is extensively utilized in practical fields, and opinion mining, also known as sentiment analysis, plays an important role in analyzing opinion and sentiment in texts. Methods in opinion mining generally depend on a sentiment lexicon, which is a set of predefined key words that express sentiment. Opinion mining requires proper sentiment words to be extracted in advance and has difficulty classifying sentences that imply an opinion without using any sentiment key words. This paper presents a new sentiment analysis method, based on text-based hidden Markov models (TextHMMs), for text classification that uses a sequence of words in training texts instead of a predefined sentiment lexicon. We sought to learn text patterns representing sentiment through ensemble TextHMMs. Our method defines hidden variables in TextHMMs by semantic cluster information in consideration of the co-occurrence of words, and thus calculates the sentiment orientation of sentences by fitted TextHMMs. To reflect diverse patterns, we applied an ensemble of TextHMM-based classifiers. In the experiments with a benchmark data set, we show that this method is superior to some existing methods and particularly has potential to classify implicit opinions. We also demonstrate the practicality of the proposed method in a real-life data set of online market reviews. (C) 2017 Elsevier Ltd. All rights reserved. | en_US |
dc.description.sponsorship | This research was supported by the grant (NRF-2015S1A5A2A03047963) funded by the Ministry of Education and National Research Foundation of Korea. This research was also supported by the National Safety Promotion Technology Development Program (201600000002094, Smart crime prevention solution development through machine learning based on Image Big Data), funded by the Ministry of Trade, Industry and Energy (MOTIE). This research was also supported by the grant (C0507566) funded by Small and Medium Business Administration (SMBA) in the Republic of Korea and Korea Association of University, Research Institute and Industry (AURI). | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | en_US |
dc.subject | Opinion mining | en_US |
dc.subject | Sentiment analysis | en_US |
dc.subject | Hidden Markov models | en_US |
dc.subject | Ensemble | en_US |
dc.subject | Boosting | en_US |
dc.subject | Clustering | en_US |
dc.title | Opinion mining using ensemble text hidden Markov models for text classification | en_US |
dc.type | Article | en_US |
dc.relation.volume | 94 | - |
dc.identifier.doi | 10.1016/j.eswa.2017.07.019 | - |
dc.relation.page | 218-227 | - |
dc.relation.journal | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.contributor.googleauthor | Kang, Mangi | - |
dc.contributor.googleauthor | Ahn, Jaelim | - |
dc.contributor.googleauthor | Lee, Kichun | - |
dc.contributor.googleauthor | Kang, Mangi | - |
dc.contributor.googleauthor | Ahn, Jaelim | - |
dc.contributor.googleauthor | Lee, Kichun | - |
dc.relation.code | 2018008560 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF INDUSTRIAL ENGINEERING | - |
dc.identifier.pid | skylee | - |
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