Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism (2022.lrec-1)
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| Challenge: | a recent study has established sentiment analysis as an alluring problem, but many feelings are left unexplored. |
| Approach: | They propose a framework to learn the polarity of emotions from Twitter posts . they compare optimism detection with sentiment analysis and hate speech detection . |
| Outcome: | The proposed framework differs between optimistic and pessimistic users on the Optimism/Pessimism Twitter dataset. |
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| Challenge: | Existing studies that focus on stance detection ignore the speech act, toxic, and moral features of tweets or lack an efficient architecture to detect the attitudes across targets. |
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Diana P. Madera-Espíndola, Zoe Caballero-Domínguez, Valeria J. Ramírez-Macías, Sabur Butt, Hector Ceballos
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| Challenge: | Modern NLP systems are typically ill-equipped when applied to noisy user-generated text. |
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| Challenge: | Empathy is the link between self and others. |
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| Challenge: | Existing methods to analyze tweets are based on lexical features and a multi-channel convolutional neural architecture. |
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| Challenge: | Existing work on document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes. |
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| Challenge: | In recent years, emotion detection in text has become more popular due to its potential applications in fields such as psychology, marketing, political science, among others. |
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