Abstract
To study the sentiment diffusion of online public opinions about hot events, we collected people’s posts through web data mining techniques. We calculated the sentiment value of each post based on a sentiment dictionary. Next, we divided those posts into five different orientations of sentiments: strongly positive (P), weakly positive (p), neutral (o), weakly negative (n), and strongly negative (N). These sentiments are combined into modes through coarse graining. We constructed sentiment mode complex network of online public opinions (SMCOP) with modes as nodes and the conversion relation in chronological order between different types of modes as edges. We calculated the strength, k-plex clique, clustering coefficient and betweenness centrality of the SMCOP. The results show that the strength distribution obeys power law. Most posts’ sentiments are weakly positive and neutral, whereas few are strongly negative. There are weakly positive subgroups and neutral subgroups with ppppp and ooooo as the core mode, respectively. Few modes have larger betweenness centrality values and most modes convert to each other with these higher betweenness centrality modes as mediums. Therefore, the relevant person or institutes can take measures to lead people’s sentiments regarding online hot events according to the sentiment diffusion mechanism.
Highlights
With the development of Internet, millions of web users spend hours a day on the website, especially on social networks
We selected five sentiment symbols as a unit and transformed the sentiment series CT of online public opinions about "3Q war" event to 4,199 sentiment modes, which consist of 731 types of modes
The results indicate that the weakly positive and neutral sentiments are the main modes in sentiment mode diffusion of online public opinions and negative modes don’t exist
Summary
With the development of Internet, millions of web users spend hours a day on the website, especially on social networks. People read news and write their views and opinions about online hot events and commodities [1]. These opinions and views reflect people’s sentiments. The sentiments in social networks can affect people’s purchase behavior [2], the sellers’ marketing plan [3], political trends [4, 5] and effectively forecast stock market [6]. Many online hot events change trends with time going by. With the development of the events and increasing comments, the sentiment of users contained in the comments influences each
Sign-in/Register to view highlights & summary 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.
