Matches in SemOpenAlex for { <https://semopenalex.org/work/W4224305765> ?p ?o ?g. }
- W4224305765 endingPage "e896" @default.
- W4224305765 startingPage "e896" @default.
- W4224305765 abstract "Urdu is a widely used language in South Asia and worldwide. While there are similar datasets available in English, we created the first multi-label emotion dataset consisting of 6,043 tweets and six basic emotions in the Urdu Nastalíq script. A multi-label (ML) classification approach was adopted to detect emotions from Urdu. The morphological and syntactic structure of Urdu makes it a challenging problem for multi-label emotion detection. In this paper, we build a set of baseline classifiers such as machine learning algorithms (Random forest (RF), Decision tree (J48), Sequential minimal optimization (SMO), AdaBoostM1, and Bagging), deep-learning algorithms (Convolutional Neural Networks (1D-CNN), Long short-term memory (LSTM), and LSTM with CNN features) and transformer-based baseline (BERT). We used a combination of text representations: stylometric-based features, pre-trained word embedding, word-based n-grams, and character-based n-grams. The paper highlights the annotation guidelines, dataset characteristics and insights into different methodologies used for Urdu based emotion classification. We present our best results using micro-averaged F1, macro-averaged F1, accuracy, Hamming loss (HL) and exact match (EM) for all tested methods." @default.
- W4224305765 created "2022-04-26" @default.
- W4224305765 creator A5008287867 @default.
- W4224305765 creator A5017713556 @default.
- W4224305765 creator A5049701126 @default.
- W4224305765 creator A5050757271 @default.
- W4224305765 creator A5054982958 @default.
- W4224305765 creator A5063747030 @default.
- W4224305765 date "2022-04-22" @default.
- W4224305765 modified "2023-10-01" @default.
- W4224305765 title "Multi-label emotion classification of Urdu tweets" @default.
- W4224305765 cites W1966797434 @default.
- W4224305765 cites W2003653478 @default.
- W4224305765 cites W2019575783 @default.
- W4224305765 cites W2023736093 @default.
- W4224305765 cites W2038178664 @default.
- W4224305765 cites W2053154970 @default.
- W4224305765 cites W2054151502 @default.
- W4224305765 cites W2064230935 @default.
- W4224305765 cites W2064675550 @default.
- W4224305765 cites W2066064791 @default.
- W4224305765 cites W2089367555 @default.
- W4224305765 cites W2091084672 @default.
- W4224305765 cites W2159094788 @default.
- W4224305765 cites W2182096631 @default.
- W4224305765 cites W2784314843 @default.
- W4224305765 cites W2788906882 @default.
- W4224305765 cites W2798989012 @default.
- W4224305765 cites W2900517206 @default.
- W4224305765 cites W2911964244 @default.
- W4224305765 cites W2952338554 @default.
- W4224305765 cites W2964236337 @default.
- W4224305765 cites W2969026449 @default.
- W4224305765 cites W2976855540 @default.
- W4224305765 cites W2979599767 @default.
- W4224305765 cites W2980021808 @default.
- W4224305765 cites W2997990748 @default.
- W4224305765 cites W3025878701 @default.
- W4224305765 cites W3041202055 @default.
- W4224305765 cites W3077780682 @default.
- W4224305765 cites W3081987387 @default.
- W4224305765 cites W3087484287 @default.
- W4224305765 cites W3095536082 @default.
- W4224305765 cites W3130111320 @default.
- W4224305765 cites W3135510479 @default.
- W4224305765 cites W3138539755 @default.
- W4224305765 cites W3147751175 @default.
- W4224305765 cites W3176558250 @default.
- W4224305765 cites W3201449892 @default.
- W4224305765 cites W3201752972 @default.
- W4224305765 cites W4212883601 @default.
- W4224305765 cites W4287121298 @default.
- W4224305765 cites W4287606455 @default.
- W4224305765 cites W4288242639 @default.
- W4224305765 cites W4302419287 @default.
- W4224305765 cites W792626264 @default.
- W4224305765 doi "https://doi.org/10.7717/peerj-cs.896" @default.
- W4224305765 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35494831" @default.
- W4224305765 hasPublicationYear "2022" @default.
- W4224305765 type Work @default.
- W4224305765 citedByCount "9" @default.
- W4224305765 countsByYear W42243057652022 @default.
- W4224305765 countsByYear W42243057652023 @default.
- W4224305765 crossrefType "journal-article" @default.
- W4224305765 hasAuthorship W4224305765A5008287867 @default.
- W4224305765 hasAuthorship W4224305765A5017713556 @default.
- W4224305765 hasAuthorship W4224305765A5049701126 @default.
- W4224305765 hasAuthorship W4224305765A5050757271 @default.
- W4224305765 hasAuthorship W4224305765A5054982958 @default.
- W4224305765 hasAuthorship W4224305765A5063747030 @default.
- W4224305765 hasBestOaLocation W42243057651 @default.
- W4224305765 hasConcept C119857082 @default.
- W4224305765 hasConcept C138885662 @default.
- W4224305765 hasConcept C153180895 @default.
- W4224305765 hasConcept C154945302 @default.
- W4224305765 hasConcept C169258074 @default.
- W4224305765 hasConcept C204321447 @default.
- W4224305765 hasConcept C2777350258 @default.
- W4224305765 hasConcept C41008148 @default.
- W4224305765 hasConcept C41895202 @default.
- W4224305765 hasConcept C81363708 @default.
- W4224305765 hasConceptScore W4224305765C119857082 @default.
- W4224305765 hasConceptScore W4224305765C138885662 @default.
- W4224305765 hasConceptScore W4224305765C153180895 @default.
- W4224305765 hasConceptScore W4224305765C154945302 @default.
- W4224305765 hasConceptScore W4224305765C169258074 @default.
- W4224305765 hasConceptScore W4224305765C204321447 @default.
- W4224305765 hasConceptScore W4224305765C2777350258 @default.
- W4224305765 hasConceptScore W4224305765C41008148 @default.
- W4224305765 hasConceptScore W4224305765C41895202 @default.
- W4224305765 hasConceptScore W4224305765C81363708 @default.
- W4224305765 hasLocation W42243057651 @default.
- W4224305765 hasLocation W42243057652 @default.
- W4224305765 hasLocation W42243057653 @default.
- W4224305765 hasLocation W42243057654 @default.
- W4224305765 hasOpenAccess W4224305765 @default.
- W4224305765 hasPrimaryLocation W42243057651 @default.
- W4224305765 hasRelatedWork W2767651786 @default.