E-word of mouth sentiment analysis for user behavior studies
• Induce and extract the emotion concept in each emotion review by introducing eight factors of sociological theory. • Opinion mining and sentiment classification analysis are based on user behavior rather than product features. • We propose a new classification of conceptual learning and natural la...
Ausführliche Beschreibung
Autor*in: |
Li, Hui [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2022 |
---|
Übergeordnetes Werk: |
Enthalten in: Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts - Feng, Yonghai ELSEVIER, 2014, an international journal, Amsterdam [u.a.] |
---|---|
Übergeordnetes Werk: |
volume:59 ; year:2022 ; number:1 ; pages:0 |
Links: |
---|
DOI / URN: |
10.1016/j.ipm.2021.102784 |
---|
Katalog-ID: |
ELV055933971 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | ELV055933971 | ||
003 | DE-627 | ||
005 | 20230624224603.0 | ||
007 | cr uuu---uuuuu | ||
008 | 220105s2022 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.ipm.2021.102784 |2 doi | |
028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001591.pica |
035 | |a (DE-627)ELV055933971 | ||
035 | |a (ELSEVIER)S0306-4573(21)00263-6 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 540 |q VZ |
082 | 0 | 4 | |a 570 |q VZ |
084 | |a 58.11 |2 bkl | ||
100 | 1 | |a Li, Hui |e verfasserin |4 aut | |
245 | 1 | 0 | |a E-word of mouth sentiment analysis for user behavior studies |
264 | 1 | |c 2022 | |
336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
337 | |a nicht spezifiziert |b z |2 rdamedia | ||
338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
520 | |a • Induce and extract the emotion concept in each emotion review by introducing eight factors of sociological theory. • Opinion mining and sentiment classification analysis are based on user behavior rather than product features. • We propose a new classification of conceptual learning and natural language processes. • The basic idea of our approach is to combine machine learning with dictionary dictionary to make multiple classifications of emotions in user behavior. | ||
700 | 1 | |a Chen, Qi |4 oth | |
700 | 1 | |a Zhong, Zhaoman |4 oth | |
700 | 1 | |a Gong, Rongrong |4 oth | |
700 | 1 | |a Han, Guokai |4 oth | |
773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Feng, Yonghai ELSEVIER |t Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts |d 2014 |d an international journal |g Amsterdam [u.a.] |w (DE-627)ELV017696526 |
773 | 1 | 8 | |g volume:59 |g year:2022 |g number:1 |g pages:0 |
856 | 4 | 0 | |u https://doi.org/10.1016/j.ipm.2021.102784 |3 Volltext |
912 | |a GBV_USEFLAG_U | ||
912 | |a GBV_ELV | ||
912 | |a SYSFLAG_U | ||
912 | |a SSG-OLC-PHA | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_70 | ||
936 | b | k | |a 58.11 |j Mechanische Verfahrenstechnik |q VZ |
951 | |a AR | ||
952 | |d 59 |j 2022 |e 1 |h 0 |
author_variant |
h l hl |
---|---|
matchkey_str |
lihuichenqizhongzhaomangongrongronghangu:2022----:wromuhetmnaayifrsr |
hierarchy_sort_str |
2022 |
bklnumber |
58.11 |
publishDate |
2022 |
allfields |
10.1016/j.ipm.2021.102784 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001591.pica (DE-627)ELV055933971 (ELSEVIER)S0306-4573(21)00263-6 DE-627 ger DE-627 rakwb eng 540 VZ 570 VZ 58.11 bkl Li, Hui verfasserin aut E-word of mouth sentiment analysis for user behavior studies 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Induce and extract the emotion concept in each emotion review by introducing eight factors of sociological theory. • Opinion mining and sentiment classification analysis are based on user behavior rather than product features. • We propose a new classification of conceptual learning and natural language processes. • The basic idea of our approach is to combine machine learning with dictionary dictionary to make multiple classifications of emotions in user behavior. Chen, Qi oth Zhong, Zhaoman oth Gong, Rongrong oth Han, Guokai oth Enthalten in Elsevier Science Feng, Yonghai ELSEVIER Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017696526 volume:59 year:2022 number:1 pages:0 https://doi.org/10.1016/j.ipm.2021.102784 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_23 GBV_ILN_70 58.11 Mechanische Verfahrenstechnik VZ AR 59 2022 1 0 |
spelling |
10.1016/j.ipm.2021.102784 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001591.pica (DE-627)ELV055933971 (ELSEVIER)S0306-4573(21)00263-6 DE-627 ger DE-627 rakwb eng 540 VZ 570 VZ 58.11 bkl Li, Hui verfasserin aut E-word of mouth sentiment analysis for user behavior studies 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Induce and extract the emotion concept in each emotion review by introducing eight factors of sociological theory. • Opinion mining and sentiment classification analysis are based on user behavior rather than product features. • We propose a new classification of conceptual learning and natural language processes. • The basic idea of our approach is to combine machine learning with dictionary dictionary to make multiple classifications of emotions in user behavior. Chen, Qi oth Zhong, Zhaoman oth Gong, Rongrong oth Han, Guokai oth Enthalten in Elsevier Science Feng, Yonghai ELSEVIER Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017696526 volume:59 year:2022 number:1 pages:0 https://doi.org/10.1016/j.ipm.2021.102784 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_23 GBV_ILN_70 58.11 Mechanische Verfahrenstechnik VZ AR 59 2022 1 0 |
allfields_unstemmed |
10.1016/j.ipm.2021.102784 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001591.pica (DE-627)ELV055933971 (ELSEVIER)S0306-4573(21)00263-6 DE-627 ger DE-627 rakwb eng 540 VZ 570 VZ 58.11 bkl Li, Hui verfasserin aut E-word of mouth sentiment analysis for user behavior studies 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Induce and extract the emotion concept in each emotion review by introducing eight factors of sociological theory. • Opinion mining and sentiment classification analysis are based on user behavior rather than product features. • We propose a new classification of conceptual learning and natural language processes. • The basic idea of our approach is to combine machine learning with dictionary dictionary to make multiple classifications of emotions in user behavior. Chen, Qi oth Zhong, Zhaoman oth Gong, Rongrong oth Han, Guokai oth Enthalten in Elsevier Science Feng, Yonghai ELSEVIER Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017696526 volume:59 year:2022 number:1 pages:0 https://doi.org/10.1016/j.ipm.2021.102784 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_23 GBV_ILN_70 58.11 Mechanische Verfahrenstechnik VZ AR 59 2022 1 0 |
allfieldsGer |
10.1016/j.ipm.2021.102784 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001591.pica (DE-627)ELV055933971 (ELSEVIER)S0306-4573(21)00263-6 DE-627 ger DE-627 rakwb eng 540 VZ 570 VZ 58.11 bkl Li, Hui verfasserin aut E-word of mouth sentiment analysis for user behavior studies 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Induce and extract the emotion concept in each emotion review by introducing eight factors of sociological theory. • Opinion mining and sentiment classification analysis are based on user behavior rather than product features. • We propose a new classification of conceptual learning and natural language processes. • The basic idea of our approach is to combine machine learning with dictionary dictionary to make multiple classifications of emotions in user behavior. Chen, Qi oth Zhong, Zhaoman oth Gong, Rongrong oth Han, Guokai oth Enthalten in Elsevier Science Feng, Yonghai ELSEVIER Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017696526 volume:59 year:2022 number:1 pages:0 https://doi.org/10.1016/j.ipm.2021.102784 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_23 GBV_ILN_70 58.11 Mechanische Verfahrenstechnik VZ AR 59 2022 1 0 |
allfieldsSound |
10.1016/j.ipm.2021.102784 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001591.pica (DE-627)ELV055933971 (ELSEVIER)S0306-4573(21)00263-6 DE-627 ger DE-627 rakwb eng 540 VZ 570 VZ 58.11 bkl Li, Hui verfasserin aut E-word of mouth sentiment analysis for user behavior studies 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Induce and extract the emotion concept in each emotion review by introducing eight factors of sociological theory. • Opinion mining and sentiment classification analysis are based on user behavior rather than product features. • We propose a new classification of conceptual learning and natural language processes. • The basic idea of our approach is to combine machine learning with dictionary dictionary to make multiple classifications of emotions in user behavior. Chen, Qi oth Zhong, Zhaoman oth Gong, Rongrong oth Han, Guokai oth Enthalten in Elsevier Science Feng, Yonghai ELSEVIER Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts 2014 an international journal Amsterdam [u.a.] (DE-627)ELV017696526 volume:59 year:2022 number:1 pages:0 https://doi.org/10.1016/j.ipm.2021.102784 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_23 GBV_ILN_70 58.11 Mechanische Verfahrenstechnik VZ AR 59 2022 1 0 |
language |
English |
source |
Enthalten in Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts Amsterdam [u.a.] volume:59 year:2022 number:1 pages:0 |
sourceStr |
Enthalten in Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts Amsterdam [u.a.] volume:59 year:2022 number:1 pages:0 |
format_phy_str_mv |
Article |
bklname |
Mechanische Verfahrenstechnik |
institution |
findex.gbv.de |
dewey-raw |
540 |
isfreeaccess_bool |
false |
container_title |
Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts |
authorswithroles_txt_mv |
Li, Hui @@aut@@ Chen, Qi @@oth@@ Zhong, Zhaoman @@oth@@ Gong, Rongrong @@oth@@ Han, Guokai @@oth@@ |
publishDateDaySort_date |
2022-01-01T00:00:00Z |
hierarchy_top_id |
ELV017696526 |
dewey-sort |
3540 |
id |
ELV055933971 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV055933971</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624224603.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220105s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ipm.2021.102784</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001591.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV055933971</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0306-4573(21)00263-6</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">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">58.11</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Hui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">E-word of mouth sentiment analysis for user behavior studies</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• Induce and extract the emotion concept in each emotion review by introducing eight factors of sociological theory. • Opinion mining and sentiment classification analysis are based on user behavior rather than product features. • We propose a new classification of conceptual learning and natural language processes. • The basic idea of our approach is to combine machine learning with dictionary dictionary to make multiple classifications of emotions in user behavior.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Qi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhong, Zhaoman</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gong, Rongrong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Han, Guokai</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Feng, Yonghai ELSEVIER</subfield><subfield code="t">Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts</subfield><subfield code="d">2014</subfield><subfield code="d">an international journal</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV017696526</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:59</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.ipm.2021.102784</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">58.11</subfield><subfield code="j">Mechanische Verfahrenstechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">59</subfield><subfield code="j">2022</subfield><subfield code="e">1</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
author |
Li, Hui |
spellingShingle |
Li, Hui ddc 540 ddc 570 bkl 58.11 E-word of mouth sentiment analysis for user behavior studies |
authorStr |
Li, Hui |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV017696526 |
format |
electronic Article |
dewey-ones |
540 - Chemistry & allied sciences 570 - Life sciences; biology |
delete_txt_mv |
keep |
author_role |
aut |
collection |
elsevier |
remote_str |
true |
illustrated |
Not Illustrated |
topic_title |
540 VZ 570 VZ 58.11 bkl E-word of mouth sentiment analysis for user behavior studies |
topic |
ddc 540 ddc 570 bkl 58.11 |
topic_unstemmed |
ddc 540 ddc 570 bkl 58.11 |
topic_browse |
ddc 540 ddc 570 bkl 58.11 |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
zu |
author2_variant |
q c qc z z zz r g rg g h gh |
hierarchy_parent_title |
Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts |
hierarchy_parent_id |
ELV017696526 |
dewey-tens |
540 - Chemistry 570 - Life sciences; biology |
hierarchy_top_title |
Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)ELV017696526 |
title |
E-word of mouth sentiment analysis for user behavior studies |
ctrlnum |
(DE-627)ELV055933971 (ELSEVIER)S0306-4573(21)00263-6 |
title_full |
E-word of mouth sentiment analysis for user behavior studies |
author_sort |
Li, Hui |
journal |
Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts |
journalStr |
Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
500 - Science |
recordtype |
marc |
publishDateSort |
2022 |
contenttype_str_mv |
zzz |
container_start_page |
0 |
author_browse |
Li, Hui |
container_volume |
59 |
class |
540 VZ 570 VZ 58.11 bkl |
format_se |
Elektronische Aufsätze |
author-letter |
Li, Hui |
doi_str_mv |
10.1016/j.ipm.2021.102784 |
dewey-full |
540 570 |
title_sort |
e-word of mouth sentiment analysis for user behavior studies |
title_auth |
E-word of mouth sentiment analysis for user behavior studies |
abstract |
• Induce and extract the emotion concept in each emotion review by introducing eight factors of sociological theory. • Opinion mining and sentiment classification analysis are based on user behavior rather than product features. • We propose a new classification of conceptual learning and natural language processes. • The basic idea of our approach is to combine machine learning with dictionary dictionary to make multiple classifications of emotions in user behavior. |
abstractGer |
• Induce and extract the emotion concept in each emotion review by introducing eight factors of sociological theory. • Opinion mining and sentiment classification analysis are based on user behavior rather than product features. • We propose a new classification of conceptual learning and natural language processes. • The basic idea of our approach is to combine machine learning with dictionary dictionary to make multiple classifications of emotions in user behavior. |
abstract_unstemmed |
• Induce and extract the emotion concept in each emotion review by introducing eight factors of sociological theory. • Opinion mining and sentiment classification analysis are based on user behavior rather than product features. • We propose a new classification of conceptual learning and natural language processes. • The basic idea of our approach is to combine machine learning with dictionary dictionary to make multiple classifications of emotions in user behavior. |
collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_23 GBV_ILN_70 |
container_issue |
1 |
title_short |
E-word of mouth sentiment analysis for user behavior studies |
url |
https://doi.org/10.1016/j.ipm.2021.102784 |
remote_bool |
true |
author2 |
Chen, Qi Zhong, Zhaoman Gong, Rongrong Han, Guokai |
author2Str |
Chen, Qi Zhong, Zhaoman Gong, Rongrong Han, Guokai |
ppnlink |
ELV017696526 |
mediatype_str_mv |
z |
isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth oth oth oth |
doi_str |
10.1016/j.ipm.2021.102784 |
up_date |
2024-07-06T18:56:36.685Z |
_version_ |
1803857110433267712 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV055933971</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624224603.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220105s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ipm.2021.102784</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001591.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV055933971</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0306-4573(21)00263-6</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">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">58.11</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Hui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">E-word of mouth sentiment analysis for user behavior studies</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• Induce and extract the emotion concept in each emotion review by introducing eight factors of sociological theory. • Opinion mining and sentiment classification analysis are based on user behavior rather than product features. • We propose a new classification of conceptual learning and natural language processes. • The basic idea of our approach is to combine machine learning with dictionary dictionary to make multiple classifications of emotions in user behavior.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Qi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhong, Zhaoman</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gong, Rongrong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Han, Guokai</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Feng, Yonghai ELSEVIER</subfield><subfield code="t">Selective oxidation of 1,2-propanediol to lactic acid catalyzed by nanosized Mg(OH)2-supported bimetallic Au–Pd catalysts</subfield><subfield code="d">2014</subfield><subfield code="d">an international journal</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV017696526</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:59</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.ipm.2021.102784</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">58.11</subfield><subfield code="j">Mechanische Verfahrenstechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">59</subfield><subfield code="j">2022</subfield><subfield code="e">1</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
score |
7.397932 |