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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Vaccine hesitancy and other COVID-19-related concerns and complaints in thePhilippines are evident on social media. It is important to identify thesedifferent topics and sentiments in order to gauge public opinion, use theinsights to develop policies, and make necessary adjustments or actions toimprove public image and reputation of the administering agency and theCOVID-19 vaccines themselves. This paper proposes a semi-supervised machinelearning pipeline to perform topic modeling, sentiment analysis, and ananalysis of vaccine brand reputation to obtain an in-depth understanding ofnational public opinion of Filipinos on Facebook. The methodology makes use ofa multilingual version of Bidirectional Encoder Representations fromTransformers or BERT for topic modeling, hierarchical clustering, fivedifferent classifiers for sentiment analysis, and cosine similarity of BERTtopic embeddings for vaccine brand reputation analysis. Results suggest thatany type of COVID-19 misinformation is an emergent property of COVID-19 publicopinion, and that the detection of COVID-19 misinformation can be anunsupervised task. Sentiment analysis aided by hierarchical clustering revealthat 21 of the 25 topics extrapolated by topic modeling are negative topics.Such negative comments spike in count whenever the Department of Health in thePhilippines posts about the COVID-19 situation in other countries.Additionally, the high numbers of laugh reactions on the Facebook posts by thesame agency -- without any humorous content -- suggest that the reactors ofthese posts tend to react the way they do, not because of what the posts areabout but because of who posted them.

Topic Modeling, Clade-assisted Sentiment Analysis, and Vaccine Brand  Reputation Analysis of COVID-19 Vaccine-related Facebook Comments in the  Philippines