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This study introduces a novel method for building multimodal dialogue sentiment analysis corpora using Internet of Things (IoT) devices and Mobile Edge Computing (MEC). The approach leverages MEC for efficient data storage and processing, validating a new deep learning sentiment analysis model.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Machine Learning

Background:

  • Dialogue sentiment analysis is a key area in AI, requiring robust multimodal corpora.
  • The Internet of Things (IoT) offers new avenues for collecting diverse, multiparty dialogue data.
  • Mobile Edge Computing (MEC) provides a distributed platform ideal for handling large datasets and complex computations.

Purpose of the Study:

  • To propose a novel procedure for constructing multimodal corpora on MEC servers.
  • To develop and train a deep learning sentiment analysis model using the constructed corpus.
  • To validate the model's effectiveness using real-world IoT-collected data.

Main Methods:

  • Proposed a procedure for multimodal corpus construction leveraging MEC's distributed storage.
  • Developed a deep learning-based sentiment analysis model.
  • Trained the model on the MEC platform, utilizing its distributed computing power.
  • Conducted experiments using a real-world dataset from IoT devices.

Main Results:

  • Successfully constructed a multimodal corpus on MEC servers.
  • The deep learning sentiment analysis model achieved validated effectiveness.
  • Demonstrated the feasibility of using MEC for corpus construction and model training.

Conclusions:

  • The proposed method effectively utilizes MEC for multimodal corpus construction and sentiment analysis model training.
  • The developed deep learning model shows strong performance for dialogue sentiment analysis.
  • This approach offers a scalable and efficient solution for advancing AI in dialogue understanding.