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

People are sharing their opinions, stories and reviews through online videosharing websites every day. Studying sentiment and subjectivity in theseopinion videos is experiencing a growing attention from academia and industry.While sentiment analysis has been successful for text, it is an understudiedresearch question for videos and multimedia content. The biggest setbacks forstudies in this direction are lack of a proper dataset, methodology, baselinesand statistical analysis of how information from different modality sourcesrelate to each other. This paper introduces to the scientific community thefirst opinion-level annotated corpus of sentiment and subjectivity analysis inonline videos called Multimodal Opinion-level Sentiment Intensity dataset(MOSI). The dataset is rigorously annotated with labels for subjectivity,sentiment intensity, per-frame and per-opinion annotated visual features, andper-milliseconds annotated audio features. Furthermore, we present baselinesfor future studies in this direction as well as a new multimodal fusionapproach that jointly models spoken words and visual gestures.

MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos