Special Issue "Emerging Application of Sentiment Analysis Technologies"
Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 24351
Interests: natural language processing (NLP); multilingual and crosslingual NLP with an emphasis on low-resource languages; sentiment analysis and opinion mining on social media; information retrieval techniques applying NLP
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Interests: natural language processing (NLP); parsing; NLP for low-resource languages; sentiment analysis and opinion mining; question answering
Sentiment analysis (SA) is the field of natural language processing that deals with the automatic comprehension of opinions shared by users across different media. The traditional application of SA is to scan the opinions of users written on social networks and websites to find out how people perceive a product or service—which aspects are working and which have to be improved. This has caused a surge of technological solutions for SA, leading to the development of a wide range of systems, from those based on knowledge that makes use of lexical, syntactic and semantic information to those that are based on supervised learning through the creation of large pre-trained models of deep neural networks. These systems allow SA to be applied to areas that go beyond the product perceptions and written text.
Current challenges include analyzing texts in which segments written in one language are mixed with segments written in another language (code switching) or in which some elements are written in a language using morphological and syntactic structures from another language (code mixing); jointly analyzing the information provided by images and video together with the associated text (captions, subtitles, descriptions, etc.), so common in the web nowadays; extending sentiment analysis techniques so that they are not only able to determine people’s emotions but also their behavior, to aid in the prevention of sexism, racism, abuse and harassment on social networks; determining people’s mental health in order to alert serious conditions, such as depression and suicidal tendencies; to give just a few examples. In this Special Issue, we thus focus on approaches defining models and resources to deal with new challenging applications of SA technology in an effective manner.
- Multilingual sentiment analysis;
- Sentiment analysis for low-resource languages;
- Multimedia sentiment analysis from video, image and text;
- Sentiment analysis for political, social and economic analysis;
- Sentiment analysis for security monitoring;
- Sentiment analysis for detecting sexism, racism, bullying and harassment;
- Sentiment analysis for health;
- Language resources for emerging applications of SA.
Technical Program Committee Members:
- Prof. Dr. Carlos Gómez-Rodríguez, Universidade da Coruña
- Prof. Dr. Jesús Vilares, Universidade da Coruña
Prof. Dr. Miguel A. Alonso
Prof. David Vilares
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- Multilingual sentiment analysis
- Sentiment analysis for low-resource languages
- Multimedia sentiment analysis from video, image and text
- Sentiment analysis for political, social and economic analysis
- Sentiment analysis for security monitoring
- Sentiment analysis for detecting sexism, bullying and harassment
- Sentiment analysis for health
- Language resources for emerging applications of SA