FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2893591486> ?p ?o ?g. }

• W2893591486 endingPage "12" @default.
• W2893591486 startingPage "1" @default.
• W2893591486 abstract "Modern companies generate value by digitalizing their services and products. Knowing what customers are saying about the firm through reviews in social media content constitutes a key factor to succeed in the big data era. However, social media data analysis is a complex discipline due to the subjectivity in text review and the additional features in raw data. Some frameworks proposed in the existing literature involve many steps that thereby increase their complexity. A two-stage framework to tackle this problem is proposed: the first stage is focused on data preparation and finding an optimal machine learning model for this data; the second stage relies on established layers of big data architectures focused on getting an outcome of data by taking most of the machine learning model of stage one. Thus, a first stage is proposed to analyze big and small datasets in a non-big data environment, whereas the second stage analyzes big datasets by applying the first stage machine learning model of. Then, a study case is presented for the first stage of the framework to analyze reviews of hotel-related businesses. Several machine learning algorithms were trained for two, three and five classes, with the best results being found for binary classification." @default.
• W2893591486 created "2018-10-05" @default.
• W2893591486 creator A5004732284 @default.
• W2893591486 creator A5006079410 @default.
• W2893591486 creator A5008658598 @default.
• W2893591486 creator A5057286696 @default.
• W2893591486 date "2019-02-01" @default.
• W2893591486 modified "2023-10-01" @default.
• W2893591486 title "Towards a big data framework for analyzing social media content" @default.
• W2893591486 cites W1042149469 @default.
• W2893591486 cites W1965449946 @default.
• W2893591486 cites W1980362203 @default.
• W2893591486 cites W1997384634 @default.
• W2893591486 cites W2010513477 @default.
• W2893591486 cites W2013167546 @default.
• W2893591486 cites W2052386534 @default.
• W2893591486 cites W2093440027 @default.
• W2893591486 cites W2132853433 @default.
• W2893591486 cites W2164848432 @default.
• W2893591486 cites W2179960895 @default.
• W2893591486 cites W2212802680 @default.
• W2893591486 cites W2215376118 @default.
• W2893591486 cites W2261525379 @default.
• W2893591486 cites W2296681290 @default.
• W2893591486 cites W2340139852 @default.
• W2893591486 cites W2407961458 @default.
• W2893591486 cites W2465701822 @default.
• W2893591486 cites W2470334367 @default.
• W2893591486 cites W2471860648 @default.
• W2893591486 cites W2498232401 @default.
• W2893591486 cites W2500086770 @default.
• W2893591486 cites W2509365102 @default.
• W2893591486 cites W2509902422 @default.
• W2893591486 cites W2519706319 @default.
• W2893591486 cites W2520361682 @default.
• W2893591486 cites W2520828129 @default.
• W2893591486 cites W2539161094 @default.
• W2893591486 cites W2576975314 @default.
• W2893591486 cites W2584429674 @default.
• W2893591486 cites W2612358188 @default.
• W2893591486 cites W2614056367 @default.
• W2893591486 cites W2615351961 @default.
• W2893591486 cites W2729974188 @default.
• W2893591486 cites W2751596245 @default.
• W2893591486 cites W2753433691 @default.
• W2893591486 cites W2754587509 @default.
• W2893591486 cites W2755977944 @default.
• W2893591486 cites W2756063524 @default.
• W2893591486 cites W2763057957 @default.
• W2893591486 cites W2763473974 @default.
• W2893591486 cites W2766647035 @default.
• W2893591486 cites W2794882009 @default.
• W2893591486 cites W2795988153 @default.
• W2893591486 cites W2963090454 @default.
• W2893591486 cites W937729739 @default.
• W2893591486 doi "https://doi.org/10.1016/j.ijinfomgt.2018.09.003" @default.
• W2893591486 hasPublicationYear "2019" @default.
• W2893591486 type Work @default.
• W2893591486 sameAs 2893591486 @default.
• W2893591486 citedByCount "85" @default.
• W2893591486 countsByYear W28935914862019 @default.
• W2893591486 countsByYear W28935914862020 @default.
• W2893591486 countsByYear W28935914862021 @default.
• W2893591486 countsByYear W28935914862022 @default.
• W2893591486 countsByYear W28935914862023 @default.
• W2893591486 crossrefType "journal-article" @default.
• W2893591486 hasAuthorship W2893591486A5004732284 @default.
• W2893591486 hasAuthorship W2893591486A5006079410 @default.
• W2893591486 hasAuthorship W2893591486A5008658598 @default.
• W2893591486 hasAuthorship W2893591486A5057286696 @default.
• W2893591486 hasBestOaLocation W28935914862 @default.
• W2893591486 hasConcept C119857082 @default.
• W2893591486 hasConcept C124101348 @default.
• W2893591486 hasConcept C132964779 @default.
• W2893591486 hasConcept C136764020 @default.
• W2893591486 hasConcept C146357865 @default.
• W2893591486 hasConcept C151730666 @default.
• W2893591486 hasConcept C154945302 @default.
• W2893591486 hasConcept C199360897 @default.
• W2893591486 hasConcept C2522767166 @default.
• W2893591486 hasConcept C26517878 @default.
• W2893591486 hasConcept C38652104 @default.
• W2893591486 hasConcept C41008148 @default.
• W2893591486 hasConcept C518677369 @default.
• W2893591486 hasConcept C75684735 @default.
• W2893591486 hasConcept C86803240 @default.
• W2893591486 hasConceptScore W2893591486C119857082 @default.
• W2893591486 hasConceptScore W2893591486C124101348 @default.
• W2893591486 hasConceptScore W2893591486C132964779 @default.
• W2893591486 hasConceptScore W2893591486C136764020 @default.
• W2893591486 hasConceptScore W2893591486C146357865 @default.
• W2893591486 hasConceptScore W2893591486C151730666 @default.
• W2893591486 hasConceptScore W2893591486C154945302 @default.
• W2893591486 hasConceptScore W2893591486C199360897 @default.
• W2893591486 hasConceptScore W2893591486C2522767166 @default.
• W2893591486 hasConceptScore W2893591486C26517878 @default.
• W2893591486 hasConceptScore W2893591486C38652104 @default.
• W2893591486 hasConceptScore W2893591486C41008148 @default.

• next