Matches in SemOpenAlex for { <https://semopenalex.org/work/W4306362235> ?p ?o ?g. }
- W4306362235 endingPage "10344" @default.
- W4306362235 startingPage "10344" @default.
- W4306362235 abstract "Numerous studies have been conducted to meet the growing need for analytic tools capable of processing increasing amounts of textual data available online, and sentiment analysis has emerged as a frontrunner in this field. Current studies are focused on the English language, while minority languages, such as Roman Urdu, are ignored because of their complex syntax and lexical varieties. In recent years, deep neural networks have become the standard in this field. The entire potential of DL models for text SA has not yet been fully explored, despite their early success. For sentiment analysis, CNN has surpassed in accuracy, although it still has some imperfections. To begin, CNNs need a significant amount of data to train. Second, it presumes that all words have the same impact on the polarity of a statement. To fill these voids, this study proposes a CNN with an attention mechanism and transfer learning to improve SA performance. Compared to state-of-the-art methods, our proposed model appears to have achieved greater classification accuracy in experiments." @default.
- W4306362235 created "2022-10-17" @default.
- W4306362235 creator A5001704713 @default.
- W4306362235 creator A5030998001 @default.
- W4306362235 creator A5032180201 @default.
- W4306362235 creator A5048709524 @default.
- W4306362235 creator A5049690302 @default.
- W4306362235 creator A5051158911 @default.
- W4306362235 creator A5063114495 @default.
- W4306362235 creator A5083808734 @default.
- W4306362235 date "2022-10-14" @default.
- W4306362235 modified "2023-10-18" @default.
- W4306362235 title "Roman Urdu Sentiment Analysis Using Transfer Learning" @default.
- W4306362235 cites W1572786359 @default.
- W4306362235 cites W2140785063 @default.
- W4306362235 cites W2203890649 @default.
- W4306362235 cites W2250539671 @default.
- W4306362235 cites W2493916176 @default.
- W4306362235 cites W2562607067 @default.
- W4306362235 cites W2565516711 @default.
- W4306362235 cites W2612843093 @default.
- W4306362235 cites W2618530766 @default.
- W4306362235 cites W2624775096 @default.
- W4306362235 cites W2761187698 @default.
- W4306362235 cites W2765753216 @default.
- W4306362235 cites W2789132801 @default.
- W4306362235 cites W2801011252 @default.
- W4306362235 cites W2807007678 @default.
- W4306362235 cites W2914363692 @default.
- W4306362235 cites W2914951395 @default.
- W4306362235 cites W2919558556 @default.
- W4306362235 cites W2927439335 @default.
- W4306362235 cites W2941723961 @default.
- W4306362235 cites W2952198026 @default.
- W4306362235 cites W2954107114 @default.
- W4306362235 cites W2954653574 @default.
- W4306362235 cites W2955109214 @default.
- W4306362235 cites W2963378656 @default.
- W4306362235 cites W2963468261 @default.
- W4306362235 cites W2966292608 @default.
- W4306362235 cites W2966614482 @default.
- W4306362235 cites W2969026449 @default.
- W4306362235 cites W2993843842 @default.
- W4306362235 cites W3000097396 @default.
- W4306362235 cites W3005825976 @default.
- W4306362235 cites W3010388538 @default.
- W4306362235 cites W3025451744 @default.
- W4306362235 cites W3028519758 @default.
- W4306362235 cites W3028860567 @default.
- W4306362235 cites W3034770463 @default.
- W4306362235 cites W3041133507 @default.
- W4306362235 cites W3087752199 @default.
- W4306362235 cites W3090154305 @default.
- W4306362235 cites W3099150152 @default.
- W4306362235 cites W3133966466 @default.
- W4306362235 cites W3159506165 @default.
- W4306362235 cites W3160464195 @default.
- W4306362235 cites W3163548027 @default.
- W4306362235 cites W3163841364 @default.
- W4306362235 cites W3183127766 @default.
- W4306362235 cites W3215822937 @default.
- W4306362235 cites W3217696222 @default.
- W4306362235 cites W4200495849 @default.
- W4306362235 cites W4200634471 @default.
- W4306362235 cites W4210253935 @default.
- W4306362235 cites W4220738400 @default.
- W4306362235 cites W4225395003 @default.
- W4306362235 cites W4225697732 @default.
- W4306362235 cites W4225906996 @default.
- W4306362235 cites W4226102240 @default.
- W4306362235 cites W4226150491 @default.
- W4306362235 cites W4281572872 @default.
- W4306362235 cites W4285107695 @default.
- W4306362235 cites W4294350997 @default.
- W4306362235 doi "https://doi.org/10.3390/app122010344" @default.
- W4306362235 hasPublicationYear "2022" @default.
- W4306362235 type Work @default.
- W4306362235 citedByCount "8" @default.
- W4306362235 countsByYear W43063622352022 @default.
- W4306362235 countsByYear W43063622352023 @default.
- W4306362235 crossrefType "journal-article" @default.
- W4306362235 hasAuthorship W4306362235A5001704713 @default.
- W4306362235 hasAuthorship W4306362235A5030998001 @default.
- W4306362235 hasAuthorship W4306362235A5032180201 @default.
- W4306362235 hasAuthorship W4306362235A5048709524 @default.
- W4306362235 hasAuthorship W4306362235A5049690302 @default.
- W4306362235 hasAuthorship W4306362235A5051158911 @default.
- W4306362235 hasAuthorship W4306362235A5063114495 @default.
- W4306362235 hasAuthorship W4306362235A5083808734 @default.
- W4306362235 hasBestOaLocation W43063622351 @default.
- W4306362235 hasConcept C108583219 @default.
- W4306362235 hasConcept C111472728 @default.
- W4306362235 hasConcept C138885662 @default.
- W4306362235 hasConcept C1491633281 @default.
- W4306362235 hasConcept C150899416 @default.
- W4306362235 hasConcept C154945302 @default.
- W4306362235 hasConcept C202444582 @default.
- W4306362235 hasConcept C204321447 @default.