Attribute embedding : learning hierarchical representations of product attributes from consumer reviews

Gespeichert in:

Autor*in:	Wang, Xin [verfasserIn] He, Jiaxiu [verfasserIn] Curry, David J. [verfasserIn] Ryoo, Jun Hyun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	attribute embedding attribute hierarchy machine learning meta-attribute natural language processing word2vec

Übergeordnetes Werk:	Enthalten in: Journal of marketing - Thousand Oaks, CA : Sage Publishing, 1936, 86(2022), 6 vom: Nov., Seite 155-175
Übergeordnetes Werk:	volume:86 ; year:2022 ; number:6 ; month:11 ; pages:155-175

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1177/00222429211047822

Katalog-ID:	1822305519

Internformat


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Indexfelder

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allfields	10.1177/00222429211047822 doi (DE-627)1822305519 (DE-599)KXP1822305519 DE-627 ger DE-627 rda eng Wang, Xin verfasserin (DE-588)1053140541 (DE-627)789626810 (DE-576)408892404 aut Attribute embedding learning hierarchical representations of product attributes from consumer reviews Xin (Shane) Wang, Jiaxiu He, David J. Curry, Jun Hyun (Joseph) Ryoo 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier attribute embedding (dpeaa)DE-206 attribute hierarchy (dpeaa)DE-206 machine learning (dpeaa)DE-206 meta-attribute (dpeaa)DE-206 natural language processing (dpeaa)DE-206 word2vec (dpeaa)DE-206 He, Jiaxiu verfasserin (DE-588)1153844958 (DE-627)1015392962 (DE-576)500595887 aut Curry, David J. verfasserin (DE-588)170249646 (DE-627)060309881 (DE-576)131150472 aut Ryoo, Jun Hyun verfasserin (DE-588)1229721320 (DE-627)1751878341 aut Enthalten in Journal of marketing Thousand Oaks, CA : Sage Publishing, 1936 86(2022), 6 vom: Nov., Seite 155-175 Online-Ressource (DE-627)33174449X (DE-600)2052318-X (DE-576)110279425 1547-7185 nnns volume:86 year:2022 number:6 month:11 pages:155-175 https://journals.sagepub.com/doi/pdf/10.1177/00222429211047822 Verlag kostenfrei https://doi.org/10.1177/00222429211047822 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_224 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2941 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 86 2022 6 11 155-175 26 01 0206 4210329975 x1z 14-11-22 2403 01 DE-LFER 422916808X 00 --%%-- --%%-- n --%%-- l01 12-12-22 2403 01 DE-LFER https://doi.org/10.1177/00222429211047822 2403 01 DE-LFER https://journals.sagepub.com/doi/pdf/10.1177/00222429211047822 26 00 DE-206 Sales, product design, and engineering teams benefit immensely from better understanding customer perspectives. How do customers combine a product's technical specifications (i.e., engineered attributes) to form abstract product benefits (i.e., meta-attributes)? To address this question, the authors use machine learning and natural language processing to develop a methodological framework that extracts a hierarchy of product attributes based on contextual information of how attributes are expressed in consumer reviews. The attribute hierarchy reveals linkages between engineered attributes and meta-attributes within a product category, enabling flexible sentiment analysis that can identify how consumers receive meta-attributes, and which engineered attributes are main drivers. The framework can guide managers to monitor only portions of review content that are relevant to specific attributes of interest. Moreover, managers can compare products within and between brands, where different names and attribute combinations are often associated with similar benefits. The authors apply the framework to the tablet computer category to generate dashboards and perceptual maps and provide validations of the attribute hierarchy using both primary and secondary data. Resultant insights allow the exploration of substantive questions, such as how Apple improved successive generations of iPads and why Hewlett-Packard and Toshiba discontinued their tablet product lines.
spelling	10.1177/00222429211047822 doi (DE-627)1822305519 (DE-599)KXP1822305519 DE-627 ger DE-627 rda eng Wang, Xin verfasserin (DE-588)1053140541 (DE-627)789626810 (DE-576)408892404 aut Attribute embedding learning hierarchical representations of product attributes from consumer reviews Xin (Shane) Wang, Jiaxiu He, David J. Curry, Jun Hyun (Joseph) Ryoo 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier attribute embedding (dpeaa)DE-206 attribute hierarchy (dpeaa)DE-206 machine learning (dpeaa)DE-206 meta-attribute (dpeaa)DE-206 natural language processing (dpeaa)DE-206 word2vec (dpeaa)DE-206 He, Jiaxiu verfasserin (DE-588)1153844958 (DE-627)1015392962 (DE-576)500595887 aut Curry, David J. verfasserin (DE-588)170249646 (DE-627)060309881 (DE-576)131150472 aut Ryoo, Jun Hyun verfasserin (DE-588)1229721320 (DE-627)1751878341 aut Enthalten in Journal of marketing Thousand Oaks, CA : Sage Publishing, 1936 86(2022), 6 vom: Nov., Seite 155-175 Online-Ressource (DE-627)33174449X (DE-600)2052318-X (DE-576)110279425 1547-7185 nnns volume:86 year:2022 number:6 month:11 pages:155-175 https://journals.sagepub.com/doi/pdf/10.1177/00222429211047822 Verlag kostenfrei https://doi.org/10.1177/00222429211047822 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_224 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2941 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 86 2022 6 11 155-175 26 01 0206 4210329975 x1z 14-11-22 2403 01 DE-LFER 422916808X 00 --%%-- --%%-- n --%%-- l01 12-12-22 2403 01 DE-LFER https://doi.org/10.1177/00222429211047822 2403 01 DE-LFER https://journals.sagepub.com/doi/pdf/10.1177/00222429211047822 26 00 DE-206 Sales, product design, and engineering teams benefit immensely from better understanding customer perspectives. How do customers combine a product's technical specifications (i.e., engineered attributes) to form abstract product benefits (i.e., meta-attributes)? To address this question, the authors use machine learning and natural language processing to develop a methodological framework that extracts a hierarchy of product attributes based on contextual information of how attributes are expressed in consumer reviews. The attribute hierarchy reveals linkages between engineered attributes and meta-attributes within a product category, enabling flexible sentiment analysis that can identify how consumers receive meta-attributes, and which engineered attributes are main drivers. The framework can guide managers to monitor only portions of review content that are relevant to specific attributes of interest. Moreover, managers can compare products within and between brands, where different names and attribute combinations are often associated with similar benefits. The authors apply the framework to the tablet computer category to generate dashboards and perceptual maps and provide validations of the attribute hierarchy using both primary and secondary data. Resultant insights allow the exploration of substantive questions, such as how Apple improved successive generations of iPads and why Hewlett-Packard and Toshiba discontinued their tablet product lines.
allfields_unstemmed	10.1177/00222429211047822 doi (DE-627)1822305519 (DE-599)KXP1822305519 DE-627 ger DE-627 rda eng Wang, Xin verfasserin (DE-588)1053140541 (DE-627)789626810 (DE-576)408892404 aut Attribute embedding learning hierarchical representations of product attributes from consumer reviews Xin (Shane) Wang, Jiaxiu He, David J. Curry, Jun Hyun (Joseph) Ryoo 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier attribute embedding (dpeaa)DE-206 attribute hierarchy (dpeaa)DE-206 machine learning (dpeaa)DE-206 meta-attribute (dpeaa)DE-206 natural language processing (dpeaa)DE-206 word2vec (dpeaa)DE-206 He, Jiaxiu verfasserin (DE-588)1153844958 (DE-627)1015392962 (DE-576)500595887 aut Curry, David J. verfasserin (DE-588)170249646 (DE-627)060309881 (DE-576)131150472 aut Ryoo, Jun Hyun verfasserin (DE-588)1229721320 (DE-627)1751878341 aut Enthalten in Journal of marketing Thousand Oaks, CA : Sage Publishing, 1936 86(2022), 6 vom: Nov., Seite 155-175 Online-Ressource (DE-627)33174449X (DE-600)2052318-X (DE-576)110279425 1547-7185 nnns volume:86 year:2022 number:6 month:11 pages:155-175 https://journals.sagepub.com/doi/pdf/10.1177/00222429211047822 Verlag kostenfrei https://doi.org/10.1177/00222429211047822 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_224 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2941 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 86 2022 6 11 155-175 26 01 0206 4210329975 x1z 14-11-22 2403 01 DE-LFER 422916808X 00 --%%-- --%%-- n --%%-- l01 12-12-22 2403 01 DE-LFER https://doi.org/10.1177/00222429211047822 2403 01 DE-LFER https://journals.sagepub.com/doi/pdf/10.1177/00222429211047822 26 00 DE-206 Sales, product design, and engineering teams benefit immensely from better understanding customer perspectives. How do customers combine a product's technical specifications (i.e., engineered attributes) to form abstract product benefits (i.e., meta-attributes)? To address this question, the authors use machine learning and natural language processing to develop a methodological framework that extracts a hierarchy of product attributes based on contextual information of how attributes are expressed in consumer reviews. The attribute hierarchy reveals linkages between engineered attributes and meta-attributes within a product category, enabling flexible sentiment analysis that can identify how consumers receive meta-attributes, and which engineered attributes are main drivers. The framework can guide managers to monitor only portions of review content that are relevant to specific attributes of interest. Moreover, managers can compare products within and between brands, where different names and attribute combinations are often associated with similar benefits. The authors apply the framework to the tablet computer category to generate dashboards and perceptual maps and provide validations of the attribute hierarchy using both primary and secondary data. Resultant insights allow the exploration of substantive questions, such as how Apple improved successive generations of iPads and why Hewlett-Packard and Toshiba discontinued their tablet product lines.
allfieldsGer	10.1177/00222429211047822 doi (DE-627)1822305519 (DE-599)KXP1822305519 DE-627 ger DE-627 rda eng Wang, Xin verfasserin (DE-588)1053140541 (DE-627)789626810 (DE-576)408892404 aut Attribute embedding learning hierarchical representations of product attributes from consumer reviews Xin (Shane) Wang, Jiaxiu He, David J. Curry, Jun Hyun (Joseph) Ryoo 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier attribute embedding (dpeaa)DE-206 attribute hierarchy (dpeaa)DE-206 machine learning (dpeaa)DE-206 meta-attribute (dpeaa)DE-206 natural language processing (dpeaa)DE-206 word2vec (dpeaa)DE-206 He, Jiaxiu verfasserin (DE-588)1153844958 (DE-627)1015392962 (DE-576)500595887 aut Curry, David J. verfasserin (DE-588)170249646 (DE-627)060309881 (DE-576)131150472 aut Ryoo, Jun Hyun verfasserin (DE-588)1229721320 (DE-627)1751878341 aut Enthalten in Journal of marketing Thousand Oaks, CA : Sage Publishing, 1936 86(2022), 6 vom: Nov., Seite 155-175 Online-Ressource (DE-627)33174449X (DE-600)2052318-X (DE-576)110279425 1547-7185 nnns volume:86 year:2022 number:6 month:11 pages:155-175 https://journals.sagepub.com/doi/pdf/10.1177/00222429211047822 Verlag kostenfrei https://doi.org/10.1177/00222429211047822 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_224 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2941 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 86 2022 6 11 155-175 26 01 0206 4210329975 x1z 14-11-22 2403 01 DE-LFER 422916808X 00 --%%-- --%%-- n --%%-- l01 12-12-22 2403 01 DE-LFER https://doi.org/10.1177/00222429211047822 2403 01 DE-LFER https://journals.sagepub.com/doi/pdf/10.1177/00222429211047822 26 00 DE-206 Sales, product design, and engineering teams benefit immensely from better understanding customer perspectives. How do customers combine a product's technical specifications (i.e., engineered attributes) to form abstract product benefits (i.e., meta-attributes)? To address this question, the authors use machine learning and natural language processing to develop a methodological framework that extracts a hierarchy of product attributes based on contextual information of how attributes are expressed in consumer reviews. The attribute hierarchy reveals linkages between engineered attributes and meta-attributes within a product category, enabling flexible sentiment analysis that can identify how consumers receive meta-attributes, and which engineered attributes are main drivers. The framework can guide managers to monitor only portions of review content that are relevant to specific attributes of interest. Moreover, managers can compare products within and between brands, where different names and attribute combinations are often associated with similar benefits. The authors apply the framework to the tablet computer category to generate dashboards and perceptual maps and provide validations of the attribute hierarchy using both primary and secondary data. Resultant insights allow the exploration of substantive questions, such as how Apple improved successive generations of iPads and why Hewlett-Packard and Toshiba discontinued their tablet product lines.
allfieldsSound	10.1177/00222429211047822 doi (DE-627)1822305519 (DE-599)KXP1822305519 DE-627 ger DE-627 rda eng Wang, Xin verfasserin (DE-588)1053140541 (DE-627)789626810 (DE-576)408892404 aut Attribute embedding learning hierarchical representations of product attributes from consumer reviews Xin (Shane) Wang, Jiaxiu He, David J. Curry, Jun Hyun (Joseph) Ryoo 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier attribute embedding (dpeaa)DE-206 attribute hierarchy (dpeaa)DE-206 machine learning (dpeaa)DE-206 meta-attribute (dpeaa)DE-206 natural language processing (dpeaa)DE-206 word2vec (dpeaa)DE-206 He, Jiaxiu verfasserin (DE-588)1153844958 (DE-627)1015392962 (DE-576)500595887 aut Curry, David J. verfasserin (DE-588)170249646 (DE-627)060309881 (DE-576)131150472 aut Ryoo, Jun Hyun verfasserin (DE-588)1229721320 (DE-627)1751878341 aut Enthalten in Journal of marketing Thousand Oaks, CA : Sage Publishing, 1936 86(2022), 6 vom: Nov., Seite 155-175 Online-Ressource (DE-627)33174449X (DE-600)2052318-X (DE-576)110279425 1547-7185 nnns volume:86 year:2022 number:6 month:11 pages:155-175 https://journals.sagepub.com/doi/pdf/10.1177/00222429211047822 Verlag kostenfrei https://doi.org/10.1177/00222429211047822 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_224 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2941 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 86 2022 6 11 155-175 26 01 0206 4210329975 x1z 14-11-22 2403 01 DE-LFER 422916808X 00 --%%-- --%%-- n --%%-- l01 12-12-22 2403 01 DE-LFER https://doi.org/10.1177/00222429211047822 2403 01 DE-LFER https://journals.sagepub.com/doi/pdf/10.1177/00222429211047822 26 00 DE-206 Sales, product design, and engineering teams benefit immensely from better understanding customer perspectives. How do customers combine a product's technical specifications (i.e., engineered attributes) to form abstract product benefits (i.e., meta-attributes)? To address this question, the authors use machine learning and natural language processing to develop a methodological framework that extracts a hierarchy of product attributes based on contextual information of how attributes are expressed in consumer reviews. The attribute hierarchy reveals linkages between engineered attributes and meta-attributes within a product category, enabling flexible sentiment analysis that can identify how consumers receive meta-attributes, and which engineered attributes are main drivers. The framework can guide managers to monitor only portions of review content that are relevant to specific attributes of interest. Moreover, managers can compare products within and between brands, where different names and attribute combinations are often associated with similar benefits. The authors apply the framework to the tablet computer category to generate dashboards and perceptual maps and provide validations of the attribute hierarchy using both primary and secondary data. Resultant insights allow the exploration of substantive questions, such as how Apple improved successive generations of iPads and why Hewlett-Packard and Toshiba discontinued their tablet product lines.
language	English
source	Enthalten in Journal of marketing 86(2022), 6 vom: Nov., Seite 155-175 volume:86 year:2022 number:6 month:11 pages:155-175
sourceStr	Enthalten in Journal of marketing 86(2022), 6 vom: Nov., Seite 155-175 volume:86 year:2022 number:6 month:11 pages:155-175
format_phy_str_mv	Article
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institution	findex.gbv.de
selectbib_iln_str_mv	26@1z 2403@01
topic_facet	attribute embedding attribute hierarchy machine learning meta-attribute natural language processing word2vec
sw_local_iln_str_mv	26: DE-206:
isfreeaccess_bool	true
container_title	Journal of marketing
authorswithroles_txt_mv	Wang, Xin @@aut@@ He, Jiaxiu @@aut@@ Curry, David J. @@aut@@ Ryoo, Jun Hyun @@aut@@
publishDateDaySort_date	2022-11-01T00:00:00Z
hierarchy_top_id	33174449X
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spellingShingle	Wang, Xin misc attribute embedding misc attribute hierarchy misc machine learning misc meta-attribute misc natural language processing misc word2vec Attribute embedding learning hierarchical representations of product attributes from consumer reviews
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topic_title	26 00 DE-206 Sales, product design, and engineering teams benefit immensely from better understanding customer perspectives. How do customers combine a product's technical specifications (i.e., engineered attributes) to form abstract product benefits (i.e., meta-attributes)? To address this question, the authors use machine learning and natural language processing to develop a methodological framework that extracts a hierarchy of product attributes based on contextual information of how attributes are expressed in consumer reviews. The attribute hierarchy reveals linkages between engineered attributes and meta-attributes within a product category, enabling flexible sentiment analysis that can identify how consumers receive meta-attributes, and which engineered attributes are main drivers. The framework can guide managers to monitor only portions of review content that are relevant to specific attributes of interest. Moreover, managers can compare products within and between brands, where different names and attribute combinations are often associated with similar benefits. The authors apply the framework to the tablet computer category to generate dashboards and perceptual maps and provide validations of the attribute hierarchy using both primary and secondary data. Resultant insights allow the exploration of substantive questions, such as how Apple improved successive generations of iPads and why Hewlett-Packard and Toshiba discontinued their tablet product lines Attribute embedding learning hierarchical representations of product attributes from consumer reviews Xin (Shane) Wang, Jiaxiu He, David J. Curry, Jun Hyun (Joseph) Ryoo attribute embedding (dpeaa)DE-206 attribute hierarchy (dpeaa)DE-206 machine learning (dpeaa)DE-206 meta-attribute (dpeaa)DE-206 natural language processing (dpeaa)DE-206 word2vec (dpeaa)DE-206
topic	misc attribute embedding misc attribute hierarchy misc machine learning misc meta-attribute misc natural language processing misc word2vec
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title	Attribute embedding learning hierarchical representations of product attributes from consumer reviews
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title_full	Attribute embedding learning hierarchical representations of product attributes from consumer reviews Xin (Shane) Wang, Jiaxiu He, David J. Curry, Jun Hyun (Joseph) Ryoo
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up_date	2024-07-04T22:53:52.144Z
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"><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://journals.sagepub.com/doi/pdf/10.1177/00222429211047822</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">Sales, product design, and engineering teams benefit immensely from better understanding customer perspectives. How do customers combine a product's technical specifications (i.e., engineered attributes) to form abstract product benefits (i.e., meta-attributes)? To address this question, the authors use machine learning and natural language processing to develop a methodological framework that extracts a hierarchy of product attributes based on contextual information of how attributes are expressed in consumer reviews. The attribute hierarchy reveals linkages between engineered attributes and meta-attributes within a product category, enabling flexible sentiment analysis that can identify how consumers receive meta-attributes, and which engineered attributes are main drivers. The framework can guide managers to monitor only portions of review content that are relevant to specific attributes of interest. Moreover, managers can compare products within and between brands, where different names and attribute combinations are often associated with similar benefits. The authors apply the framework to the tablet computer category to generate dashboards and perceptual maps and provide validations of the attribute hierarchy using both primary and secondary data. Resultant insights allow the exploration of substantive questions, such as how Apple improved successive generations of iPads and why Hewlett-Packard and Toshiba discontinued their tablet product lines.</subfield></datafield></record></collection>
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Attribute embedding : learning hierarchical representations of product attributes from consumer reviews

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