Abstract
Twitter plays a significant role in information diffusion, and it has evolved to become an important information resource as well as news feed. There is a widespread interest in what is happening on Twitter, and the instantaneous news information that is passed on. However, with the large amount of data, it is impossible to manually determine what topic is trending, which makes real-time topic detection attractive and significant. Furthermore, Twitter provides a platform for the sharing of opinions and providing feedback for events, news, and products, etc. Because users tend to express their real thoughts on Twitter, it is recognized as a valuable source of opinions. Nevertheless, most works about trending topic detection fail to consider sentiments. In this work, we develop a non-parametric supervised real-time-trending topic-detection model with a sentimental feature. By performing experiments, we show that our model successfully detects trending sentimental topic in a short time. After applying a combination of multiple features, e.g., tweet volume and user volume, the proposed model demonstrates impressive effectiveness with an 82.3% recall rate, surpassing all of the competitors.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
