Matches in SemOpenAlex for { <https://semopenalex.org/work/W3201610432> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W3201610432 endingPage "156" @default.
- W3201610432 startingPage "146" @default.
- W3201610432 abstract "In online interactions, users frequently add emojis (e.g., smileys, hearts, angry faces) to text for expressing the emotions behind the communication context, aiming at a better interpretation to text especially of polysemous short expressions. Emotion recognition refers to the automated process of identifying and classifying human emotions. If text-based emoticons (i.e., emojis created by textual symbols and characters) can be directly understood by semantic-based context recognition tools used in the Web and Artificial Intelligence and robotics, image-based emojis need instead image recognition for a complete semantic context interpretation. This study aims to explore and compare systematically different classification models of emoticon pictograms collected from the Internet, with different labels according to the Ekman model of six basic emotions. A first comparison involves supervised machine learning classifiers trained on features extracted through neural networks. In the second phase, the comparison is extended to different deep learning models. Results indicate that deep learning models performed excellent, and traditional supervised algorithms also achieve very promising outcomes." @default.
- W3201610432 created "2021-09-27" @default.
- W3201610432 creator A5005797686 @default.
- W3201610432 creator A5022438396 @default.
- W3201610432 creator A5052630971 @default.
- W3201610432 date "2021-01-01" @default.
- W3201610432 modified "2023-09-26" @default.
- W3201610432 title "Emojis Pictogram Classification for Semantic Recognition of Emotional Context" @default.
- W3201610432 cites W1966797434 @default.
- W3201610432 cites W2097117768 @default.
- W3201610432 cites W2136132422 @default.
- W3201610432 cites W2172000360 @default.
- W3201610432 cites W2194775991 @default.
- W3201610432 cites W2337522860 @default.
- W3201610432 cites W2908671501 @default.
- W3201610432 cites W2916894829 @default.
- W3201610432 cites W2992060548 @default.
- W3201610432 cites W3000698475 @default.
- W3201610432 cites W3043400672 @default.
- W3201610432 doi "https://doi.org/10.1007/978-3-030-86993-9_14" @default.
- W3201610432 hasPublicationYear "2021" @default.
- W3201610432 type Work @default.
- W3201610432 sameAs 3201610432 @default.
- W3201610432 citedByCount "3" @default.
- W3201610432 countsByYear W32016104322022 @default.
- W3201610432 crossrefType "book-chapter" @default.
- W3201610432 hasAuthorship W3201610432A5005797686 @default.
- W3201610432 hasAuthorship W3201610432A5022438396 @default.
- W3201610432 hasAuthorship W3201610432A5052630971 @default.
- W3201610432 hasConcept C119857082 @default.
- W3201610432 hasConcept C138885662 @default.
- W3201610432 hasConcept C151730666 @default.
- W3201610432 hasConcept C154945302 @default.
- W3201610432 hasConcept C193125573 @default.
- W3201610432 hasConcept C199360897 @default.
- W3201610432 hasConcept C204321447 @default.
- W3201610432 hasConcept C2779343474 @default.
- W3201610432 hasConcept C41008148 @default.
- W3201610432 hasConcept C41895202 @default.
- W3201610432 hasConcept C527412718 @default.
- W3201610432 hasConcept C7220189 @default.
- W3201610432 hasConcept C86803240 @default.
- W3201610432 hasConceptScore W3201610432C119857082 @default.
- W3201610432 hasConceptScore W3201610432C138885662 @default.
- W3201610432 hasConceptScore W3201610432C151730666 @default.
- W3201610432 hasConceptScore W3201610432C154945302 @default.
- W3201610432 hasConceptScore W3201610432C193125573 @default.
- W3201610432 hasConceptScore W3201610432C199360897 @default.
- W3201610432 hasConceptScore W3201610432C204321447 @default.
- W3201610432 hasConceptScore W3201610432C2779343474 @default.
- W3201610432 hasConceptScore W3201610432C41008148 @default.
- W3201610432 hasConceptScore W3201610432C41895202 @default.
- W3201610432 hasConceptScore W3201610432C527412718 @default.
- W3201610432 hasConceptScore W3201610432C7220189 @default.
- W3201610432 hasConceptScore W3201610432C86803240 @default.
- W3201610432 hasLocation W32016104321 @default.
- W3201610432 hasOpenAccess W3201610432 @default.
- W3201610432 hasPrimaryLocation W32016104321 @default.
- W3201610432 hasRelatedWork W11298561 @default.
- W3201610432 hasRelatedWork W11991885 @default.
- W3201610432 hasRelatedWork W12553087 @default.
- W3201610432 hasRelatedWork W13360413 @default.
- W3201610432 hasRelatedWork W14379156 @default.
- W3201610432 hasRelatedWork W1788602 @default.
- W3201610432 hasRelatedWork W4727329 @default.
- W3201610432 hasRelatedWork W505434 @default.
- W3201610432 hasRelatedWork W8956168 @default.
- W3201610432 hasRelatedWork W8411197 @default.
- W3201610432 isParatext "false" @default.
- W3201610432 isRetracted "false" @default.
- W3201610432 magId "3201610432" @default.
- W3201610432 workType "book-chapter" @default.