AI Empowered Sentiment Analysis
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 20045
Interests: urban computing; mobile computing; network science
Special Issues, Collections and Topics in MDPI journals
Interests: computational social sciences; AI IoT; smart cities
Data on various Internet platforms, which contain valuable information that is helpful for decision making, are growing explosively. Sentiment analysis aims to extract and analyze people’s attitudes toward opinion targets. However, due to the large amount of data, quick and accurate completion of sentiment analysis is still challenging. The application of AI technology has greatly promoted the development of sentiment analysis. Traditional sentiment analysis mainly relies on manpower, a process which is not only time-consuming and laborious but also unable to analyze sentiment comprehensively and accurately. The advantage of artificial intelligence lies in the integration and utilization of big data technology, which can adopt an automatic coding mode to classify and summarize sentiment colors and comprehensively improve the functionality of the algorithm. By making use of intelligent analysis, identification and judgment in artificial intelligence, accurate recognition and analysis of sentiment can be realized. In order to improve AI applications in sentiment analysis, new theories, technologies, architectures, algorithms and mechanisms are needed. This Special Issue aims to gather relevant research from industry and academia detailing the latest findings and developments in the field of AI for sentiment analysis. We invite high-quality paper submissions of theoretical and experimental nature on topics including, but not limited to: multi-modal sentiment analysis; aspect-based sentiment analysis; resources for sentiment analysis; transfer learning for sentiment analysis; and sentiment-controlled text generation.
Prof. Dr. Xiangjie Kong
Prof. Dr. Wei Wang
Dr. Han Liu
Manuscript Submission Information
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- artificial intelligence technology
- sentiment analysis
- opinion mining
- deep learning
- natural language processing