English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We analyze the problem of majority sentiment detection in Online SocialNetworks (OSN), and relate the detection error probability to the underlyinggraph of the OSN. Modeling the underlying social network as an Ising Markovrandom field prior based on a given graph, we show that in the case of theempty graph (independent sentiments) and the chain graph, the detection isalways inaccurate, even when the number of users grow to infinity. In the caseof the complete graph, the detection is inaccurate if the connection strengthis below a certain critical value, while it is asymptotically accurate if thestrength is above that critical value, which is analogous to the phasetransition phenomenon in statistical physics.

Asymptotic Performance Analysis of Majority Sentiment Detection in Online Social Networks