Vote for your favorite mineral in #MinCup25! - Carpathite vs. Tugtupite
Battle of mineralogical oddities as organic mineral #carpathite battles against vs #tugtupite, a mineral that looks like reindeer blood!
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Lai, Chin-Hui, Tseng, Kuo-Chiuan (2022) Applying Deep Learning Models to Analyze Users’ Aspects, Sentiment, and Semantic Features for Product Recommendation. Applied Sciences, 12 (4) 2118pp. doi:10.3390/app12042118

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleApplying Deep Learning Models to Analyze Users’ Aspects, Sentiment, and Semantic Features for Product Recommendation
JournalApplied Sciences
AuthorsLai, Chin-HuiAuthor
Tseng, Kuo-ChiuanAuthor
Year2022 (February 17)Volume12
Issue4
PublisherMDPI AG
DOIdoi:10.3390/app12042118Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID13876600Long-form Identifiermindat:1:5:13876600:1
GUID0
Full ReferenceLai, Chin-Hui, Tseng, Kuo-Chiuan (2022) Applying Deep Learning Models to Analyze Users’ Aspects, Sentiment, and Semantic Features for Product Recommendation. Applied Sciences, 12 (4) 2118pp. doi:10.3390/app12042118
Plain TextLai, Chin-Hui, Tseng, Kuo-Chiuan (2022) Applying Deep Learning Models to Analyze Users’ Aspects, Sentiment, and Semantic Features for Product Recommendation. Applied Sciences, 12 (4) 2118pp. doi:10.3390/app12042118
In(2022, February) Applied Sciences Vol. 12 (4) MDPI AG

See Also

These are possibly similar items as determined by title/reference text matching only.

Aslam, Naila, Xia, Kewen, Rustam, Furqan, Hameed, Afifa, Ashraf, Imran (2022) Using Aspect-Level Sentiments for Calling App Recommendation with Hybrid Deep-Learning Models. Applied Sciences, 12 (17) 8522 doi:10.3390/app12178522
Chandrasekaran, Ganesh, Antoanela, Naaji, Andrei, Gabor, Monica, Ciobanu, Hemanth, Jude (2022) Visual Sentiment Analysis Using Deep Learning Models with Social Media Data. Applied Sciences, 12 (3) 1030pp. doi:10.3390/app12031030
Huang, Chien-Yi, Lin, I-Chen, Liu, Yuan-Lien (2022) Applying Deep Learning to Construct a Defect Detection System for Ceramic Substrates. Applied Sciences, 12 (5) 2269pp. doi:10.3390/app12052269
Singh, Chetanpal, Imam, Tasadduq, Wibowo, Santoso, Grandhi, Srimannarayana (2022) A Deep Learning Approach for Sentiment Analysis of COVID-19 Reviews. Applied Sciences, 12 (8) 3709pp. doi:10.3390/app12083709
Park, KwangJin, Choi, YoungJin, Lee, HongChul (2022) COVID-19 CXR Classification: Applying Domain Extension Transfer Learning and Deep Learning. Applied Sciences, 12 (21) 10715 doi:10.3390/app122110715
Yang, Ming-Der, Tseng, Hsin-Hung, Hsu, Yu-Chun, Tsai, Hui Ping (2020) Semantic Segmentation Using Deep Learning with Vegetation Indices for Rice Lodging Identification in Multi-date UAV Visible Images. Remote Sensing, 12 (4). doi:10.3390/rs12040633
Park, So-Hyun, Kim, Mi-Yeon, Kim, Yeon-Ji, Park, Young-Ho (2022) A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea. Applied Sciences, 12 (4) 1916pp. doi:10.3390/app12041916
Saleh, Hager, Mostafa, Sherif, Gabralla, Lubna Abdelkareim, O. Aseeri, Ahmad, El-Sappagh, Shaker (2022) Enhanced Arabic Sentiment Analysis Using a Novel Stacking Ensemble of Hybrid and Deep Learning Models. Applied Sciences, 12 (18) 8967 doi:10.3390/app12188967
Cordeiro-Costas, Moisés, Villanueva, Daniel, Eguía-Oller, Pablo, Granada-Álvarez, Enrique (2022) Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting. Applied Sciences, 12 (17) 8769 doi:10.3390/app12178769
Zhou, Quanqiang, Huang, Cheng, Duan, Liangliang (2022) Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks Optimization. Applied Sciences, 12 (19) 10135 doi:10.3390/app121910135
Banbhrani, Santosh Kumar, Xu, Bo, Lin, Hongfei, Sajnani, Dileep Kumar (2022) Spider Taylor-ChOA: Optimized Deep Learning Based Sentiment Classification for Review Rating Prediction. Applied Sciences, 12 (7) 3211pp. doi:10.3390/app12073211
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 20, 2025 01:30:23
Go to top of page