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

Sentiment analysis, mostly based on text, has been rapidly developing in thelast decade and has attracted widespread attention in both academia andindustry. However, the information in the real world usually comes frommultiple modalities, such as audio and text. Therefore, in this paper, based onaudio and text, we consider the task of multimodal sentiment analysis andpropose a novel fusion strategy including both multi-feature fusion andmulti-modality fusion to improve the accuracy of audio-text sentiment analysis.We call it the DFF-ATMF (Deep Feature Fusion - Audio and Text Modality Fusion)model, which consists of two parallel branches, the audio modality based branchand the text modality based branch. Its core mechanisms are the fusion ofmultiple feature vectors and multiple modality attention. Experiments on theCMU-MOSI dataset and the recently released CMU-MOSEI dataset, both collectedfrom YouTube for sentiment analysis, show the very competitive results of ourDFF-ATMF model. Furthermore, by virtue of attention weight distributionheatmaps, we also demonstrate the deep features learned by using DFF-ATMF arecomplementary to each other and robust. Surprisingly, DFF-ATMF also achievesnew state-of-the-art results on the IEMOCAP dataset, indicating that theproposed fusion strategy also has a good generalization ability for multimodalemotion recognition.

Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis