AI Empowered Sentiment Analysis
With the popularity of the social media, a large amount of user-generated content, such as comments, is emerging, which is crucial for all industries. Recently, the development of deep learning and computing power have made it possible to handle complex data. However, there are still some including...
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| Format: | Online |
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| Sprog: | engelsk |
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MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Online adgang: | ONIX_20240906_9783725818235_26 |
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| collection | Directory of Open Access Books |
| description | With the popularity of the social media, a large amount of user-generated content, such as comments, is emerging, which is crucial for all industries. Recently, the development of deep learning and computing power have made it possible to handle complex data. However, there are still some including (but are not limited to): (1) How can we construct a multi-modal sentiment analysis framework? (2) How can we accurately extract aspect–sentiment quadruples? (3) How can we generate fine-grained sentiment text? To tackle these challenges, this Special Issue focuses on multi-modal sentiment analysis, aspect–sentiment extraction, interpretability, and so on. In the following, we briefly summarize the selected two papers that we believe will make significant contributions. (1) "Generative Aspect Sentiment Quad Prediction with Self-Inference Template" by Li et al., considered that current research predominantly confines templates to single sentences, limiting the model’s reasoning opportunities. Therefore, the authors introduce a self-inference template (SIT) to guide the model in thoughtful reasoning. (2) "Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction" by Sun et al., proposes a new sentiment cue extraction (SCE) self-supervised framework, aimed at improving the interpretability of models. In conclusion, we extend our heartfelt appreciation to all the authors and reviewers who selflessly put their energy to ensure the successful completion of this Special Issue. |
| format | Online |
| id | doab-20.500.12854ir-143664 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1436642024-09-06T07:59:08Z AI Empowered Sentiment Analysis Kong, Xiangjie Wang, Wei Liu, Han artificial intelligence natural language processing controllable text generation review generation pre-trained language model fine-grained sentiment word embeddings BERT sentiment analysis convolutional neural network sentiment lexicon autoregressive model customer reviews deep learning emotion analysis optimized classification review text for online courses attention mechanism gating mechanism ASTE biaffine attention structure-biased BERT GCN linguistic feature aspect-level sentiment analysis graph attention network feature extract scene generation story visualization GAN story understanding language learning personality traits text analytics machine learning MBTI COVID-19 social media Reddit emotions resilience multimodal emotion recognition feature extraction feature-level fusion speaker recognition font recommendation system content emotion analysis emotion calculation models usability evaluation emotion-based font recommendation multimodality triplet extraction Graph Neural Networks sentiment cue extraction self-supervised learning interpretable machine learning aspect-based sentiment analysis aspect sentiment quad prediction aspect-category-opinion-sentiment chain of thought prompt thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science With the popularity of the social media, a large amount of user-generated content, such as comments, is emerging, which is crucial for all industries. Recently, the development of deep learning and computing power have made it possible to handle complex data. However, there are still some including (but are not limited to): (1) How can we construct a multi-modal sentiment analysis framework? (2) How can we accurately extract aspect–sentiment quadruples? (3) How can we generate fine-grained sentiment text? To tackle these challenges, this Special Issue focuses on multi-modal sentiment analysis, aspect–sentiment extraction, interpretability, and so on. In the following, we briefly summarize the selected two papers that we believe will make significant contributions. (1) "Generative Aspect Sentiment Quad Prediction with Self-Inference Template" by Li et al., considered that current research predominantly confines templates to single sentences, limiting the model’s reasoning opportunities. Therefore, the authors introduce a self-inference template (SIT) to guide the model in thoughtful reasoning. (2) "Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction" by Sun et al., proposes a new sentiment cue extraction (SCE) self-supervised framework, aimed at improving the interpretability of models. In conclusion, we extend our heartfelt appreciation to all the authors and reviewers who selflessly put their energy to ensure the successful completion of this Special Issue. 2024-09-06T07:59:01Z 2024-09-06T07:59:01Z 2024 book ONIX_20240906_9783725818235_26 9783725818235 9783725818242 https://directory.doabooks.org/handle/20.500.12854/143664 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/9672 https://mdpi.com/books/pdfview/book/9672 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-1824-2 10.3390/books978-3-7258-1824-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725818235 9783725818242 266 open access |
| spellingShingle | artificial intelligence natural language processing controllable text generation review generation pre-trained language model fine-grained sentiment word embeddings BERT sentiment analysis convolutional neural network sentiment lexicon autoregressive model customer reviews deep learning emotion analysis optimized classification review text for online courses attention mechanism gating mechanism ASTE biaffine attention structure-biased BERT GCN linguistic feature aspect-level sentiment analysis graph attention network feature extract scene generation story visualization GAN story understanding language learning personality traits text analytics machine learning MBTI COVID-19 social media emotions resilience multimodal emotion recognition feature extraction feature-level fusion speaker recognition font recommendation system content emotion analysis emotion calculation models usability evaluation emotion-based font recommendation multimodality triplet extraction Graph Neural Networks sentiment cue extraction self-supervised learning interpretable machine learning aspect-based sentiment analysis aspect sentiment quad prediction aspect-category-opinion-sentiment chain of thought prompt thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science AI Empowered Sentiment Analysis |
| title | AI Empowered Sentiment Analysis |
| title_full | AI Empowered Sentiment Analysis |
| title_fullStr | AI Empowered Sentiment Analysis |
| title_full_unstemmed | AI Empowered Sentiment Analysis |
| title_short | AI Empowered Sentiment Analysis |
| title_sort | ai empowered sentiment analysis |
| topic | artificial intelligence natural language processing controllable text generation review generation pre-trained language model fine-grained sentiment word embeddings BERT sentiment analysis convolutional neural network sentiment lexicon autoregressive model customer reviews deep learning emotion analysis optimized classification review text for online courses attention mechanism gating mechanism ASTE biaffine attention structure-biased BERT GCN linguistic feature aspect-level sentiment analysis graph attention network feature extract scene generation story visualization GAN story understanding language learning personality traits text analytics machine learning MBTI COVID-19 social media emotions resilience multimodal emotion recognition feature extraction feature-level fusion speaker recognition font recommendation system content emotion analysis emotion calculation models usability evaluation emotion-based font recommendation multimodality triplet extraction Graph Neural Networks sentiment cue extraction self-supervised learning interpretable machine learning aspect-based sentiment analysis aspect sentiment quad prediction aspect-category-opinion-sentiment chain of thought prompt thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | artificial intelligence natural language processing controllable text generation review generation pre-trained language model fine-grained sentiment word embeddings BERT sentiment analysis convolutional neural network sentiment lexicon autoregressive model customer reviews deep learning emotion analysis optimized classification review text for online courses attention mechanism gating mechanism ASTE biaffine attention structure-biased BERT GCN linguistic feature aspect-level sentiment analysis graph attention network feature extract scene generation story visualization GAN story understanding language learning personality traits text analytics machine learning MBTI COVID-19 social media emotions resilience multimodal emotion recognition feature extraction feature-level fusion speaker recognition font recommendation system content emotion analysis emotion calculation models usability evaluation emotion-based font recommendation multimodality triplet extraction Graph Neural Networks sentiment cue extraction self-supervised learning interpretable machine learning aspect-based sentiment analysis aspect sentiment quad prediction aspect-category-opinion-sentiment chain of thought prompt thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | ONIX_20240906_9783725818235_26 |