How to Create Effective and Efficient Naḥwu Media with Short Videos Based on the Camtasia Application?

Handayani, Eka Utari; Utami, Rika Lutfhiana; Tamsil, Irvan Maulana

doi:10.17509/alsuniyat.v4i1.29232

Cited by 1 publication

(2 citation statements)

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“…The public datasets are tested on two datasets, CMU-MOSI and CMU-MOSE. These datasets contain video clips, audio clips, and corresponding text clips from YouTube; each video clip is annotated with human annotations for its sentiment classification, and the sentiment classification values range from [ 1 – 5 ] to represent a total of five levels.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…With the continuous emergence of various media and short videos [ 1 , 2 ] in recent years, the impact on children's emotions [ 3 , 4 ] in daily life is getting bigger and bigger, such as popular music and videos published on YouTube [ 5 , 6 ], TikTok [ 7 , 8 ], and other platforms. Often these data contain three types of modalities, namely, video [ 9 , 10 ], audio [ 11 , 12 ], and text information [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Modal Convolutional Neural Network Model for Intelligent Analysis of the Influence of Music Genres on Children’s Emotions

Qian

Chen

2022

Computational Intelligence and Neuroscience

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The influence of music genres on children’s emotional intelligence is one of the hot topics in the field of multi-modal emotion research. How to fuse multi-modal information has an important impact on children’s emotional analysis. Most of the current research is based on transformer, in which the self-attention mechanism module is improved to achieve the fusion effect of multi-modal information. However, it is difficult for these methods to effectively capture the effective information of different modalities. Therefore, for the task of the influence of music genres on children’s emotions, this paper proposes a transformer-based multi-modal convolutional neural network. The first is to use the BiLSTM sub-network model to extract the video and audio features and use the BERT sub-network to extract the text features. Secondly, this paper uses the improved transformer cross-modal fusion module to effectively fuse different types of modal information. Finally, the transformer module is used to judge the information of different modalities and analyze the emotion from the multi-modal information. At the same time, a large number of experiments prove that the model based on multi-modal convolutional neural network proposed in this paper surpasses other methods in prediction accuracy and effectively improves the accuracy of sentiment classification tasks.

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Section: Experiments and Analysismentioning

confidence: 99%