![]() ![]() arXiv:1903.10676īengio Y, Ducharme R, Vincent P (2000) A neural probabilistic language model. ![]() arXiv:1905.09642īeltagy I, Lo K, Cohan A (2019) Scibert: a pretrained language model for scientific text. In: Proceedings of the 28th international conference on computational linguistics, pp 225–243īataa E, Wu J (2019) An investigation of transfer learning-based sentiment analysis in Japanese. In: Proceedings of the second workshop on figurative language processing, pp 98–103īabanejad N, Davoudi H, An A, Papagelis M (2020) Affective and contextual embedding for sarcasm detection. ![]() arXiv:2101.10038Īvvaru A, Vobilisetty S, Mamidi,R (2020) Detecting sarcasm in conversation context using transformer-based models. arXiv:1908.05054Īlhuzali H, Ananiadou S (2021) Spanemo: casting multi-label emotion classification as span-prediction. IEEE Access 5:16173–16192Īlberti C, Ling J, Collins M, Reitter D (2019) Fusion of detected objects in text for visual question answering. Appl Soft Comput 107:107373Īl-Moslmi T, Omar N, Abdullah S, Albared M (2017) Approaches to cross-domain sentiment analysis: a systematic literature review. IEEE, pp 117–121Īgüero-Torales MM, Salas JIA, López-Herrera AG (2021) Deep learning and multilingual sentiment analysis on social media data: an overview. In: 2020 17th international computer conference on wavelet active media technology and information processing (ICCWAMTIP). Artif Intell Rev 54:1–41Īdoma AF, Henry NM, Chen W (2020) Comparative analyses of bert, roberta, distilbert, and xlnet for text-based emotion recognition. Inf Fusion 76:204–226Īcheampong FA, Nunoo-Mensah H, Chen W (2021) Transformer models for text-based emotion detection: a review of bert-based approaches. This study aims to discuss the background of sequential transfer learning, review the evolution of pretrained models, extend the literature with the application of sequential transfer learning to different sentiment analysis tasks (aspect-based sentiment analysis, multimodal sentiment analysis, sarcasm detection, cross-domain sentiment classification, multilingual sentiment analysis, emotion detection) and suggest future research directions on model compression, effective knowledge adaptation techniques, neutrality detection and ambivalence handling tasks.Ībdu SA, Yousef AH, Salem A (2021) Multimodal video sentiment analysis using deep learning approaches, a survey. To this end, sequential transfer learning provided a solution to alleviate the training bottleneck issues of data scarcity and facilitate sentiment analysis application. However, supervised deep learning methods are known to be data hungry, but insufficient training data in practice may cause the application to be impractical. Previous pieces of literature mostly focus on reviewing the application of various deep learning models to sentiment analysis. Recently, sequential transfer learning emerged as a modern technique for applying the “pretrain then fine-tune” paradigm to leverage existing knowledge to improve the performance of various downstream NLP tasks, with no exception of sentiment analysis. ![]()
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