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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Sentiment Analysis of Image with Text Caption using Deep Learning Techniques.

Pavan Kumar Chaubey1, Tarun Kumar Arora2, K Bhavana Raj3

  • 1Department of Applied Sciences Engineering, Tula's Institute, Dhoolkot, Dehradun, Uttarakhand, India.

Computational Intelligence and Neuroscience
|July 7, 2022
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Summary
This summary is machine-generated.

This study introduces a deep learning-based intermodal analysis to combine image and text sentiment analysis for better opinion prediction on social media. The new model shows improved accuracy and reduced errors compared to existing methods.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Social media use has shifted towards visual content (images, videos, GIFs) alongside text.
  • Sentiment analysis research is expanding to include visual media for opinion prediction.
  • Existing text and image sentiment analysis methods have limitations in capturing multimodal nuances.

Purpose of the Study:

  • To propose a novel deep learning-based intermodal (DLBI) analysis technique.
  • To investigate the combined analysis of image sentiment and text captions for opinion prediction.
  • To enhance the accuracy and efficiency of social media sentiment analysis.

Main Methods:

  • Utilized the VGG network to extract opinion information in numerical vector form.
  • Applied an active deep learning approach for predicting future views based on information vectors.
  • Developed an intermodal analysis technique to link textual and visual data.

Main Results:

  • The proposed DLBI model demonstrated superior performance over alternative methods.
  • Achieved higher accuracy and precision in sentiment analysis.
  • Showcased reduced latency and error rates in opinion prediction.

Conclusions:

  • Combining image and text sentiment analysis via DLBI offers a more robust approach to understanding social media opinions.
  • The developed model provides a significant advancement in multimodal sentiment analysis.
  • Further research into intermodal analysis is recommended for comprehensive opinion mining.