Matches in SemOpenAlex for { <https://semopenalex.org/work/W2765936481> ?p ?o ?g. }
Showing items 1 to 99 of
99
with 100 items per page.
- W2765936481 endingPage "683" @default.
- W2765936481 startingPage "672" @default.
- W2765936481 abstract "Sentiment analysis is an increasingly important area in NLP to extract opinions and sentiment expressed by humans. Traditional methods are often difficult to tackle the problems of different sample distribution and domain dependence, which seriously limits the development of sentiment classification. In this paper, a novel sentiment analysis method is proposed by combining improved Adaboost and transfer learning based on Gaussian Processes to solve these two problems. A Paragraph Vector Model is employed to obtain the continuous distributed vector representations. Then, Adaboost method is used to choose the most important training features in source training data and auxiliary data. Finally, an asymmetric transfer learning classifier is introduced in Gaussian Processes. It is shown that, compared with the existing algorithms, our method is more effective for the different sample distribution and domain dependence." @default.
- W2765936481 created "2017-11-10" @default.
- W2765936481 creator A5006865197 @default.
- W2765936481 creator A5020746135 @default.
- W2765936481 creator A5035562422 @default.
- W2765936481 date "2017-01-01" @default.
- W2765936481 modified "2023-09-23" @default.
- W2765936481 title "Sentiment Analysis with Improved Adaboost and Transfer Learning Based on Gaussian Process" @default.
- W2765936481 cites W2022853402 @default.
- W2765936481 cites W2031998113 @default.
- W2765936481 cites W2081801689 @default.
- W2765936481 cites W2084046180 @default.
- W2765936481 cites W2107474859 @default.
- W2765936481 cites W2112483442 @default.
- W2765936481 cites W2115575686 @default.
- W2765936481 cites W2122838776 @default.
- W2765936481 cites W2155328222 @default.
- W2765936481 cites W2162852363 @default.
- W2765936481 cites W2165698076 @default.
- W2765936481 cites W2166706824 @default.
- W2765936481 cites W2218755335 @default.
- W2765936481 cites W2290551858 @default.
- W2765936481 cites W2335703454 @default.
- W2765936481 cites W2344811709 @default.
- W2765936481 cites W2395579298 @default.
- W2765936481 cites W2415969251 @default.
- W2765936481 cites W2514769532 @default.
- W2765936481 cites W2516564751 @default.
- W2765936481 cites W2536583325 @default.
- W2765936481 cites W2602420801 @default.
- W2765936481 cites W2612769033 @default.
- W2765936481 doi "https://doi.org/10.1007/978-3-319-68542-7_58" @default.
- W2765936481 hasPublicationYear "2017" @default.
- W2765936481 type Work @default.
- W2765936481 sameAs 2765936481 @default.
- W2765936481 citedByCount "0" @default.
- W2765936481 crossrefType "book-chapter" @default.
- W2765936481 hasAuthorship W2765936481A5006865197 @default.
- W2765936481 hasAuthorship W2765936481A5020746135 @default.
- W2765936481 hasAuthorship W2765936481A5035562422 @default.
- W2765936481 hasConcept C119857082 @default.
- W2765936481 hasConcept C121332964 @default.
- W2765936481 hasConcept C12267149 @default.
- W2765936481 hasConcept C134306372 @default.
- W2765936481 hasConcept C136764020 @default.
- W2765936481 hasConcept C141404830 @default.
- W2765936481 hasConcept C150899416 @default.
- W2765936481 hasConcept C153180895 @default.
- W2765936481 hasConcept C154945302 @default.
- W2765936481 hasConcept C163716315 @default.
- W2765936481 hasConcept C185592680 @default.
- W2765936481 hasConcept C198531522 @default.
- W2765936481 hasConcept C2777206241 @default.
- W2765936481 hasConcept C33923547 @default.
- W2765936481 hasConcept C36503486 @default.
- W2765936481 hasConcept C41008148 @default.
- W2765936481 hasConcept C43617362 @default.
- W2765936481 hasConcept C62520636 @default.
- W2765936481 hasConcept C66402592 @default.
- W2765936481 hasConcept C95623464 @default.
- W2765936481 hasConceptScore W2765936481C119857082 @default.
- W2765936481 hasConceptScore W2765936481C121332964 @default.
- W2765936481 hasConceptScore W2765936481C12267149 @default.
- W2765936481 hasConceptScore W2765936481C134306372 @default.
- W2765936481 hasConceptScore W2765936481C136764020 @default.
- W2765936481 hasConceptScore W2765936481C141404830 @default.
- W2765936481 hasConceptScore W2765936481C150899416 @default.
- W2765936481 hasConceptScore W2765936481C153180895 @default.
- W2765936481 hasConceptScore W2765936481C154945302 @default.
- W2765936481 hasConceptScore W2765936481C163716315 @default.
- W2765936481 hasConceptScore W2765936481C185592680 @default.
- W2765936481 hasConceptScore W2765936481C198531522 @default.
- W2765936481 hasConceptScore W2765936481C2777206241 @default.
- W2765936481 hasConceptScore W2765936481C33923547 @default.
- W2765936481 hasConceptScore W2765936481C36503486 @default.
- W2765936481 hasConceptScore W2765936481C41008148 @default.
- W2765936481 hasConceptScore W2765936481C43617362 @default.
- W2765936481 hasConceptScore W2765936481C62520636 @default.
- W2765936481 hasConceptScore W2765936481C66402592 @default.
- W2765936481 hasConceptScore W2765936481C95623464 @default.
- W2765936481 hasLocation W27659364811 @default.
- W2765936481 hasOpenAccess W2765936481 @default.
- W2765936481 hasPrimaryLocation W27659364811 @default.
- W2765936481 hasRelatedWork W1996541855 @default.
- W2765936481 hasRelatedWork W2003125512 @default.
- W2765936481 hasRelatedWork W2027103233 @default.
- W2765936481 hasRelatedWork W2041399278 @default.
- W2765936481 hasRelatedWork W2136184105 @default.
- W2765936481 hasRelatedWork W2160451891 @default.
- W2765936481 hasRelatedWork W2336974148 @default.
- W2765936481 hasRelatedWork W3013515612 @default.
- W2765936481 hasRelatedWork W2187500075 @default.
- W2765936481 hasRelatedWork W2345184372 @default.
- W2765936481 isParatext "false" @default.
- W2765936481 isRetracted "false" @default.
- W2765936481 magId "2765936481" @default.
- W2765936481 workType "book-chapter" @default.