FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4296392364> ?p ?o ?g. }

• W4296392364 endingPage "e1100" @default.
• W4296392364 startingPage "e1100" @default.
• W4296392364 abstract "The exponential rise in social media via microblogging sites like Twitter has sparked curiosity in sentiment analysis that exploits user feedback towards a targeted product or service. Considering its significance in business intelligence and decision-making, numerous efforts have been made in this area. However, lack of dictionaries, unannotated data, large-scale unstructured data, and low accuracies have plagued these approaches. Also, sentiment classification through classifier ensemble has been underexplored in literature. In this article, we propose a Semantic Relational Machine Learning (SRML) model that automatically classifies the sentiment of tweets by using classifier ensemble and optimal features. The model employs the Cascaded Feature Selection (CFS) strategy, a novel statistical assessment approach based on Wilcoxon rank sum test, univariate logistic regression assisted significant predictor test and cross-correlation test. It further uses the efficacy of word2vec-based continuous bag-of-words and n-gram feature extraction in conjunction with SentiWordNet for finding optimal features for classification. We experiment on six public Twitter sentiment datasets, the STS-Gold dataset, the Obama-McCain Debate (OMD) dataset, the healthcare reform (HCR) dataset and the SemEval2017 Task 4A, 4B and 4C on a heterogeneous classifier ensemble comprising fourteen individual classifiers from different paradigms. Results from the experimental study indicate that CFS supports in attaining a higher classification accuracy with up to 50% lesser features compared to count vectorizer approach. In Intra-model performance assessment, the Artificial Neural Network-Gradient Descent (ANN-GD) classifier performs comparatively better than other individual classifiers, but the Best Trained Ensemble (BTE) strategy outperforms on all metrics. In inter-model performance assessment with existing state-of-the-art systems, the proposed model achieved higher accuracy and outperforms more accomplished models employing quantum-inspired sentiment representation (QSR), transformer-based methods like BERT, BERTweet, RoBERTa and ensemble techniques. The research thus provides critical insights into implementing similar strategy into building more generic and robust expert system for sentiment analysis that can be leveraged across industries." @default.
• W4296392364 created "2022-09-20" @default.
• W4296392364 creator A5002844497 @default.
• W4296392364 creator A5044672544 @default.
• W4296392364 creator A5083563373 @default.
• W4296392364 date "2022-09-20" @default.
• W4296392364 modified "2023-10-16" @default.
• W4296392364 title "Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble" @default.
• W4296392364 cites W1500477536 @default.
• W4296392364 cites W1538926071 @default.
• W4296392364 cites W1841724727 @default.
• W4296392364 cites W2003303386 @default.
• W4296392364 cites W2015525779 @default.
• W4296392364 cites W2020111801 @default.
• W4296392364 cites W2084046180 @default.
• W4296392364 cites W2090132754 @default.
• W4296392364 cites W2097726431 @default.
• W4296392364 cites W2119505024 @default.
• W4296392364 cites W2131774270 @default.
• W4296392364 cites W2168505588 @default.
• W4296392364 cites W2306941105 @default.
• W4296392364 cites W2316199521 @default.
• W4296392364 cites W2334921165 @default.
• W4296392364 cites W2440899416 @default.
• W4296392364 cites W2584429674 @default.
• W4296392364 cites W2781487490 @default.
• W4296392364 cites W2913898221 @default.
• W4296392364 cites W2921875967 @default.
• W4296392364 cites W2982216702 @default.
• W4296392364 cites W3013898566 @default.
• W4296392364 cites W3018949385 @default.
• W4296392364 cites W3081987387 @default.
• W4296392364 cites W3082523222 @default.
• W4296392364 cites W3134798453 @default.
• W4296392364 cites W3146682110 @default.
• W4296392364 cites W3156510899 @default.
• W4296392364 cites W3174735216 @default.
• W4296392364 cites W3191769560 @default.
• W4296392364 cites W3198531509 @default.
• W4296392364 cites W3206749614 @default.
• W4296392364 cites W4200196767 @default.
• W4296392364 cites W4200596338 @default.
• W4296392364 cites W4205715519 @default.
• W4296392364 cites W4285740482 @default.
• W4296392364 doi "https://doi.org/10.7717/peerj-cs.1100" @default.
• W4296392364 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36262147" @default.
• W4296392364 hasPublicationYear "2022" @default.
• W4296392364 type Work @default.
• W4296392364 citedByCount "7" @default.
• W4296392364 countsByYear W42963923642023 @default.
• W4296392364 crossrefType "journal-article" @default.
• W4296392364 hasAuthorship W4296392364A5002844497 @default.
• W4296392364 hasAuthorship W4296392364A5044672544 @default.
• W4296392364 hasAuthorship W4296392364A5083563373 @default.
• W4296392364 hasBestOaLocation W42963923641 @default.
• W4296392364 hasConcept C119857082 @default.
• W4296392364 hasConcept C148483581 @default.
• W4296392364 hasConcept C154945302 @default.
• W4296392364 hasConcept C2776461190 @default.
• W4296392364 hasConcept C41008148 @default.
• W4296392364 hasConcept C41608201 @default.
• W4296392364 hasConcept C45942800 @default.
• W4296392364 hasConcept C66402592 @default.
• W4296392364 hasConcept C95623464 @default.
• W4296392364 hasConceptScore W4296392364C119857082 @default.
• W4296392364 hasConceptScore W4296392364C148483581 @default.
• W4296392364 hasConceptScore W4296392364C154945302 @default.
• W4296392364 hasConceptScore W4296392364C2776461190 @default.
• W4296392364 hasConceptScore W4296392364C41008148 @default.
• W4296392364 hasConceptScore W4296392364C41608201 @default.
• W4296392364 hasConceptScore W4296392364C45942800 @default.
• W4296392364 hasConceptScore W4296392364C66402592 @default.
• W4296392364 hasConceptScore W4296392364C95623464 @default.
• W4296392364 hasLocation W42963923641 @default.
• W4296392364 hasLocation W42963923642 @default.
• W4296392364 hasLocation W42963923643 @default.
• W4296392364 hasLocation W42963923644 @default.
• W4296392364 hasOpenAccess W4296392364 @default.
• W4296392364 hasPrimaryLocation W42963923641 @default.
• W4296392364 hasRelatedWork W2973799232 @default.
• W4296392364 hasRelatedWork W3016925281 @default.
• W4296392364 hasRelatedWork W3158264953 @default.
• W4296392364 hasRelatedWork W3192393100 @default.
• W4296392364 hasRelatedWork W3200179079 @default.
• W4296392364 hasRelatedWork W4211165872 @default.
• W4296392364 hasRelatedWork W4225307033 @default.
• W4296392364 hasRelatedWork W4288748750 @default.
• W4296392364 hasRelatedWork W4310989423 @default.
• W4296392364 hasRelatedWork W4378211550 @default.
• W4296392364 hasVolume "8" @default.
• W4296392364 isParatext "false" @default.
• W4296392364 isRetracted "false" @default.
• W4296392364 workType "article" @default.