An Integrated Deep Learning and Belief Rule-Based Expert System for Visual Sentiment Analysis under Uncertainty

Sharif Noor Zisad,Etu Chowdhury,Mohammad Shahadat Hossain,Raihan Ul Islam,Karl Andersson

doi:10.3390/a14070213

Sharif Noor Zisad, Etu Chowdhury + Show 3 more

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https://doi.org/10.3390/a14070213

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Abstract

Visual sentiment analysis has become more popular than textual ones in various domains for decision-making purposes. On account of this, we develop a visual sentiment analysis system, which can classify image expression. The system classifies images by taking into account six different expressions such as anger, joy, love, surprise, fear, and sadness. In our study, we propose an expert system by integrating a Deep Learning method with a Belief Rule Base (known as the BRB-DL approach) to assess an image’s overall sentiment under uncertainty. This BRB-DL approach includes both the data-driven and knowledge-driven techniques to determine the overall sentiment. Our integrated expert system outperforms the state-of-the-art methods of visual sentiment analysis with promising results. The integrated system can classify images with 86% accuracy. The system can be beneficial to understand the emotional tendency and psychological state of an individual.

Highlights

Algorithms 2021, 14, 213. https://In the modern communication system, people use different social media platforms (e.g., Facebook, Twitter, Instagram, and Flickr) to express their opinions on various issues and activities of their daily life
Since an integrated model performs better than a stand-alone model [9,10,11], we propose the integration of a deep learning method with Belief Rule-Based Expert System (BRBES) to improve the prediction accuracy in visual sentiment analysis
We explore a total of eight belief rule base for BRBES that takes less computational time

Summary

Introduction

In the modern communication system, people use different social media platforms (e.g., Facebook, Twitter, Instagram, and Flickr) to express their opinions on various issues and activities of their daily life. In these platforms, users can share visual content with the textual one to communicate with others. Visual sentiment analysis has become a part of our daily lives [2]

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