Enterprise Strategic Management From the Perspective of Business Ecosystem Construction Based on Multimodal Emotion Recognition

Abstract

Emotion recognition (ER) is an important part of building an intelligent human-computer interaction system and plays an important role in human-computer interaction. Often, people express their feelings through a variety of symbols, such as words and facial expressions. A business ecosystem is an economic community based on interacting organizations and individuals. Over time, they develop their capabilities and roles together and tend to develop themselves in the direction of one or more central enterprises. This paper aims to study a multimodal ER method based on attention mechanism. It analyzes the current emotional state of consumers and the development direction of enterprises through multi-modal ER of human emotions and analysis of market trends, so as to provide the most appropriate response or plan. This paper firstly describes the related methods of multimodal ER and deep learning in detail, and briefly outlines the meaning of enterprise strategy in the business ecosystem. Then, two datasets, CMU-MOSI and CMU-MOSEI, are selected to design the scheme for multimodal ER based on self-attention mechanism. Through the comparative analysis of the accuracy of single-modal and multi-modal ER, the self-attention mechanism is applied in the experiment. The experimental results show that the average recognition accuracy of happy under multimodal ER reaches 91.5%.

Enhancing Multimodal Emotion Recognition through Attention Mechanisms in BERT and CNN Architectures

“…Close on its heels came Delbrouck et al [40] with an accuracy of 81.52%, though their F1-score is not provided. A slight dip is seen in Bi et al [41], who recorded an accuracy of 75.0% and an F1-score of 74.5%. Lio et al [24] only offered an F1-score of 81.0%, omitting accuracy data.…”