Relevant data underlying the findings described in manuscript.
<p>Relevant data underlying the findings described in manuscript.</p>
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852023944627879936 |
|---|---|
| author | Lin Qi (128224) |
| author2 | Yunjie Xie (5126780) Qianqian Zhang (587342) Jian Zhang (1682) Yanhong Ma (163685) |
| author2_role | author author author author |
| author_facet | Lin Qi (128224) Yunjie Xie (5126780) Qianqian Zhang (587342) Jian Zhang (1682) Yanhong Ma (163685) |
| author_role | author |
| dc.creator.none.fl_str_mv | Lin Qi (128224) Yunjie Xie (5126780) Qianqian Zhang (587342) Jian Zhang (1682) Yanhong Ma (163685) |
| dc.date.none.fl_str_mv | 2025-01-03T18:35:17Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0316277.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Relevant_data_underlying_the_findings_described_in_manuscript_/28133116 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Cancer Biological Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> e dataset containing 262 commerce faces challenges 752 online reviews model &# 8217 predict perceived risk influence perceived risk extract thematic features analyzing review content feature extraction phase perceived risk thematic features review content risk samples prediction phase online shopping textcnn model predictive model content homogenization feature fusion feature characteristics critical feature xgboost algorithm website information test set skincare products skin safety significant differences paper aims merchant characteristics interpretability analysis f1 score electronic products different contexts 86 %, 85 %. 84 %, |
| dc.title.none.fl_str_mv | Relevant data underlying the findings described in manuscript. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Relevant data underlying the findings described in manuscript.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_bdb5c331d05c7d2d081029a2d4bdb655 |
| identifier_str_mv | 10.1371/journal.pone.0316277.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28133116 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Relevant data underlying the findings described in manuscript.Lin Qi (128224)Yunjie Xie (5126780)Qianqian Zhang (587342)Jian Zhang (1682)Yanhong Ma (163685)BiotechnologyCancerBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedxlink "> edataset containing 262commerce faces challenges752 online reviewsmodel &# 8217predict perceived riskinfluence perceived riskextract thematic featuresanalyzing review contentfeature extraction phaseperceived riskthematic featuresreview contentrisk samplesprediction phaseonline shoppingtextcnn modelpredictive modelcontent homogenizationfeature fusionfeature characteristicscritical featurexgboost algorithmwebsite informationtest setskincare productsskin safetysignificant differencespaper aimsmerchant characteristicsinterpretability analysisf1 scoreelectronic productsdifferent contexts86 %,85 %.84 %,<p>Relevant data underlying the findings described in manuscript.</p>2025-01-03T18:35:17ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0316277.s001https://figshare.com/articles/dataset/Relevant_data_underlying_the_findings_described_in_manuscript_/28133116CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/281331162025-01-03T18:35:17Z |
| spellingShingle | Relevant data underlying the findings described in manuscript. Lin Qi (128224) Biotechnology Cancer Biological Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> e dataset containing 262 commerce faces challenges 752 online reviews model &# 8217 predict perceived risk influence perceived risk extract thematic features analyzing review content feature extraction phase perceived risk thematic features review content risk samples prediction phase online shopping textcnn model predictive model content homogenization feature fusion feature characteristics critical feature xgboost algorithm website information test set skincare products skin safety significant differences paper aims merchant characteristics interpretability analysis f1 score electronic products different contexts 86 %, 85 %. 84 %, |
| status_str | publishedVersion |
| title | Relevant data underlying the findings described in manuscript. |
| title_full | Relevant data underlying the findings described in manuscript. |
| title_fullStr | Relevant data underlying the findings described in manuscript. |
| title_full_unstemmed | Relevant data underlying the findings described in manuscript. |
| title_short | Relevant data underlying the findings described in manuscript. |
| title_sort | Relevant data underlying the findings described in manuscript. |
| topic | Biotechnology Cancer Biological Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> e dataset containing 262 commerce faces challenges 752 online reviews model &# 8217 predict perceived risk influence perceived risk extract thematic features analyzing review content feature extraction phase perceived risk thematic features review content risk samples prediction phase online shopping textcnn model predictive model content homogenization feature fusion feature characteristics critical feature xgboost algorithm website information test set skincare products skin safety significant differences paper aims merchant characteristics interpretability analysis f1 score electronic products different contexts 86 %, 85 %. 84 %, |