Special Issue "Sentiment Analysis in Social Media Data"
A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".
Deadline for manuscript submissions: 18 December 2023 | Viewed by 3509
Special Issue Editor
Interests: computational linguistics; text processing; lexical semantics
Special Issue Information
Dear Colleagues,
The development of the Internet and its rapid popularity have made it one of our main tools for consulting and disseminating information. With the development and exponential growth of social networks, the way in which human beings relate to each other has been affected since interaction through these has become a daily task, to such an extent that we can even maintain interpersonal relationships exclusively online with the use of certain platforms. In addition, the recent COVID-19 pandemic caused an accelerated evolution of this phenomenon because we were forced to interact remotely. The use of social networks has become so popular that, according to the publication "Digital 2021"¹, approximately 57% of the world population actively uses social networks such as Twitter, and on average, we invest around 2h 27m daily into this activity. Online platforms have quickly become involved in public discourse, their algorithms helping citizens join social groups, sort through the noise of public discourse, and even keep abreast of current events.
Posts on social networks can be on any topic, and furthermore, there are few restrictions on the content of the posts (e.g., news, comments, etc.). The content of the comments is usually charged with the emotions of the person who publishes them. This emotional charge is useful for identifying the points of view of the users. Social networks give us the opportunity to understand how readers react to a variety of topics, from politics to entertainment. Some of these topics can be controversial if people debate the topic for a period of time. This Special Issue is devoted to recent research in sentiment analysis in social networks, focusing both on the creation of new resources and their applications, as well as algorithms for finding interesting patterns and social groups within them.
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¹ https://datareportal.com/reports/digital-2021-october-global-statshot
Dr. Hiram Calvo
Guest Editor
Manuscript Submission Information
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Keywords
- social groups discovery
- sentiment analysis
- emotional reactions to posts
- emotion models applications to social networks
- diachronic sentiment analysis
