Topic and Sentiment Words Extraction in Cross-Domain Product Reviews
Abstract Sentiment analysis is very popular in natural language processing and text mining. The traditional sentiment analysis methods use supervised and unsupervised classifiers in a single domain and achieve good results. When training data and test data come from different domains, these methods...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Ge [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Wireless personal communications - Springer US, 1994, 102(2018), 2 vom: 03. Jan., Seite 1773-1783 |
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| Übergeordnetes Werk: |
volume:102 ; year:2018 ; number:2 ; day:03 ; month:01 ; pages:1773-1783 |
| Links: |
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| DOI / URN: |
10.1007/s11277-017-5235-7 |
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OLC2053820349 |
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| 520 | |a Abstract Sentiment analysis is very popular in natural language processing and text mining. The traditional sentiment analysis methods use supervised and unsupervised classifiers in a single domain and achieve good results. When training data and test data come from different domains, these methods become poor. The problem of cross-domain opinion analysis is that it is not easy to get a large number of tagged data sets and it is impossible to tag all the data in the interesting domains. We propose an extraction method for topic and sentiment words based on conditional random field and syntactic structure to analyze the sentiment orientation of Chinese product reviews. We aim to extract topic and sentiment words from target domain and identify their sentiment orientation with one or a few topic and sentiment words being tagged in the source domain and words in the target domain without any tagged information. Our experimental results show that our method is effective in cross-domain sentiment analysis. | ||
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10.1007/s11277-017-5235-7 doi (DE-627)OLC2053820349 (DE-He213)s11277-017-5235-7-p DE-627 ger DE-627 rakwb eng 620 VZ Wang, Ge verfasserin aut Topic and Sentiment Words Extraction in Cross-Domain Product Reviews 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Sentiment analysis is very popular in natural language processing and text mining. The traditional sentiment analysis methods use supervised and unsupervised classifiers in a single domain and achieve good results. When training data and test data come from different domains, these methods become poor. The problem of cross-domain opinion analysis is that it is not easy to get a large number of tagged data sets and it is impossible to tag all the data in the interesting domains. We propose an extraction method for topic and sentiment words based on conditional random field and syntactic structure to analyze the sentiment orientation of Chinese product reviews. We aim to extract topic and sentiment words from target domain and identify their sentiment orientation with one or a few topic and sentiment words being tagged in the source domain and words in the target domain without any tagged information. Our experimental results show that our method is effective in cross-domain sentiment analysis. Topic word Sentiment word Cross-domain Sentiment analysis Opinion classification Pu, Pengbo aut Liang, Yongquan aut Enthalten in Wireless personal communications Springer US, 1994 102(2018), 2 vom: 03. Jan., Seite 1773-1783 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:102 year:2018 number:2 day:03 month:01 pages:1773-1783 https://doi.org/10.1007/s11277-017-5235-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 102 2018 2 03 01 1773-1783 |
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10.1007/s11277-017-5235-7 doi (DE-627)OLC2053820349 (DE-He213)s11277-017-5235-7-p DE-627 ger DE-627 rakwb eng 620 VZ Wang, Ge verfasserin aut Topic and Sentiment Words Extraction in Cross-Domain Product Reviews 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Sentiment analysis is very popular in natural language processing and text mining. The traditional sentiment analysis methods use supervised and unsupervised classifiers in a single domain and achieve good results. When training data and test data come from different domains, these methods become poor. The problem of cross-domain opinion analysis is that it is not easy to get a large number of tagged data sets and it is impossible to tag all the data in the interesting domains. We propose an extraction method for topic and sentiment words based on conditional random field and syntactic structure to analyze the sentiment orientation of Chinese product reviews. We aim to extract topic and sentiment words from target domain and identify their sentiment orientation with one or a few topic and sentiment words being tagged in the source domain and words in the target domain without any tagged information. Our experimental results show that our method is effective in cross-domain sentiment analysis. Topic word Sentiment word Cross-domain Sentiment analysis Opinion classification Pu, Pengbo aut Liang, Yongquan aut Enthalten in Wireless personal communications Springer US, 1994 102(2018), 2 vom: 03. Jan., Seite 1773-1783 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:102 year:2018 number:2 day:03 month:01 pages:1773-1783 https://doi.org/10.1007/s11277-017-5235-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 102 2018 2 03 01 1773-1783 |
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10.1007/s11277-017-5235-7 doi (DE-627)OLC2053820349 (DE-He213)s11277-017-5235-7-p DE-627 ger DE-627 rakwb eng 620 VZ Wang, Ge verfasserin aut Topic and Sentiment Words Extraction in Cross-Domain Product Reviews 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Sentiment analysis is very popular in natural language processing and text mining. The traditional sentiment analysis methods use supervised and unsupervised classifiers in a single domain and achieve good results. When training data and test data come from different domains, these methods become poor. The problem of cross-domain opinion analysis is that it is not easy to get a large number of tagged data sets and it is impossible to tag all the data in the interesting domains. We propose an extraction method for topic and sentiment words based on conditional random field and syntactic structure to analyze the sentiment orientation of Chinese product reviews. We aim to extract topic and sentiment words from target domain and identify their sentiment orientation with one or a few topic and sentiment words being tagged in the source domain and words in the target domain without any tagged information. Our experimental results show that our method is effective in cross-domain sentiment analysis. Topic word Sentiment word Cross-domain Sentiment analysis Opinion classification Pu, Pengbo aut Liang, Yongquan aut Enthalten in Wireless personal communications Springer US, 1994 102(2018), 2 vom: 03. Jan., Seite 1773-1783 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:102 year:2018 number:2 day:03 month:01 pages:1773-1783 https://doi.org/10.1007/s11277-017-5235-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 102 2018 2 03 01 1773-1783 |
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10.1007/s11277-017-5235-7 doi (DE-627)OLC2053820349 (DE-He213)s11277-017-5235-7-p DE-627 ger DE-627 rakwb eng 620 VZ Wang, Ge verfasserin aut Topic and Sentiment Words Extraction in Cross-Domain Product Reviews 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Sentiment analysis is very popular in natural language processing and text mining. The traditional sentiment analysis methods use supervised and unsupervised classifiers in a single domain and achieve good results. When training data and test data come from different domains, these methods become poor. The problem of cross-domain opinion analysis is that it is not easy to get a large number of tagged data sets and it is impossible to tag all the data in the interesting domains. We propose an extraction method for topic and sentiment words based on conditional random field and syntactic structure to analyze the sentiment orientation of Chinese product reviews. We aim to extract topic and sentiment words from target domain and identify their sentiment orientation with one or a few topic and sentiment words being tagged in the source domain and words in the target domain without any tagged information. Our experimental results show that our method is effective in cross-domain sentiment analysis. Topic word Sentiment word Cross-domain Sentiment analysis Opinion classification Pu, Pengbo aut Liang, Yongquan aut Enthalten in Wireless personal communications Springer US, 1994 102(2018), 2 vom: 03. Jan., Seite 1773-1783 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:102 year:2018 number:2 day:03 month:01 pages:1773-1783 https://doi.org/10.1007/s11277-017-5235-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 102 2018 2 03 01 1773-1783 |
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Abstract Sentiment analysis is very popular in natural language processing and text mining. The traditional sentiment analysis methods use supervised and unsupervised classifiers in a single domain and achieve good results. When training data and test data come from different domains, these methods become poor. The problem of cross-domain opinion analysis is that it is not easy to get a large number of tagged data sets and it is impossible to tag all the data in the interesting domains. We propose an extraction method for topic and sentiment words based on conditional random field and syntactic structure to analyze the sentiment orientation of Chinese product reviews. We aim to extract topic and sentiment words from target domain and identify their sentiment orientation with one or a few topic and sentiment words being tagged in the source domain and words in the target domain without any tagged information. Our experimental results show that our method is effective in cross-domain sentiment analysis. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract Sentiment analysis is very popular in natural language processing and text mining. The traditional sentiment analysis methods use supervised and unsupervised classifiers in a single domain and achieve good results. When training data and test data come from different domains, these methods become poor. The problem of cross-domain opinion analysis is that it is not easy to get a large number of tagged data sets and it is impossible to tag all the data in the interesting domains. We propose an extraction method for topic and sentiment words based on conditional random field and syntactic structure to analyze the sentiment orientation of Chinese product reviews. We aim to extract topic and sentiment words from target domain and identify their sentiment orientation with one or a few topic and sentiment words being tagged in the source domain and words in the target domain without any tagged information. Our experimental results show that our method is effective in cross-domain sentiment analysis. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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Abstract Sentiment analysis is very popular in natural language processing and text mining. The traditional sentiment analysis methods use supervised and unsupervised classifiers in a single domain and achieve good results. When training data and test data come from different domains, these methods become poor. The problem of cross-domain opinion analysis is that it is not easy to get a large number of tagged data sets and it is impossible to tag all the data in the interesting domains. We propose an extraction method for topic and sentiment words based on conditional random field and syntactic structure to analyze the sentiment orientation of Chinese product reviews. We aim to extract topic and sentiment words from target domain and identify their sentiment orientation with one or a few topic and sentiment words being tagged in the source domain and words in the target domain without any tagged information. Our experimental results show that our method is effective in cross-domain sentiment analysis. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2053820349</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504080116.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11277-017-5235-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2053820349</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11277-017-5235-7-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Ge</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Topic and Sentiment Words Extraction in Cross-Domain Product Reviews</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Sentiment analysis is very popular in natural language processing and text mining. The traditional sentiment analysis methods use supervised and unsupervised classifiers in a single domain and achieve good results. When training data and test data come from different domains, these methods become poor. The problem of cross-domain opinion analysis is that it is not easy to get a large number of tagged data sets and it is impossible to tag all the data in the interesting domains. We propose an extraction method for topic and sentiment words based on conditional random field and syntactic structure to analyze the sentiment orientation of Chinese product reviews. We aim to extract topic and sentiment words from target domain and identify their sentiment orientation with one or a few topic and sentiment words being tagged in the source domain and words in the target domain without any tagged information. 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