Emotion Recognition Using Deep Learning in Pandemic with Real-time Email Alert

Dey, Arnab; Dasgupta, Kousik

doi:10.1007/978-981-16-8862-1_13

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Nawaz et al presented an aspect-based sentiment analysis technique using a POS tagger, Visuwords and a rational classifier [26]. Dey et al presented a work with a better rate of accuracy and faster recognition time for monitoring the emotional state of humans [27]. The proposed deep convolution neural network (DCNN) with a custom Gabor filter recognizes emotions with an accuracy of 85.8%.…”

Section: Mowlaei Et Al Developed Extensions Of Two Lexicon Generation...mentioning

confidence: 99%

Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

Rana¹,

Rehman²,

Nawaz³

et al. 2023

Computer Systems Science and Engineering

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People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events, public products and the latest affairs. People share their thoughts and feelings about various topics, including products, news, blogs, etc. In user reviews and tweets, sentiment analysis is used to discover opinions and feelings. Sentiment polarity is a term used to describe how sentiment is represented. Positive, neutral and negative are all examples of it. This area is still in its infancy and needs several critical upgrades. Slang and hidden emotions can detract from the accuracy of traditional techniques. Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories. Some existing strategies are domain-specific. The proposed model incorporates aspect extraction, association rule mining and the deep learning technique Bidirectional Encoder Representations from Transformers (BERT). Aspects are extracted using Part of Speech Tagger and association rule mining is used to associate aspects with opinion words. Later, classification was performed using BER. The proposed approach attained an average of 89.45% accuracy, 88.45% precision and 85.98% recall on different datasets of products and Twitter. The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

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Section: Mowlaei Et Al Developed Extensions Of Two Lexicon Generation...mentioning

confidence: 99%

Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

Rana¹,

Rehman²,

Nawaz³

et al. 2023

Computer Systems Science and Engineering

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show abstract

“…These methods work well on a large dataset problem and the angularity of faces. Nevertheless, the remaining challenge in these methods is the LR input image as the features and facial components will not be distinguishable [28,29].…”

Section: Introductionmentioning

confidence: 99%

Empowering Face Recognition Methods using a GAN-based Single Image Super-Resolution Network

Shahbakhsh¹,

Hassanpour²

2022

IJE

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Face recognition is one of the most common authentication techniques widely used due to its easy access. In many face recognition applications, captured images are often of low resolution. Face recognition methods perform poorly on low resolution images because they are trained on high resolution face images. Although existing face hallucination methods may generate visually pleasing images, they cannot improve the performance of face recognition methods at low resolution as the structure of the face image and high-frequency details are not sufficiently preserved. Recent advances in deep learning have been used in this paper to propose a new face super-resolution approach to empower face recognition methods. In this paper, a Generative Adversarial Network is used to empower face recognition in low-resolution images. This network considers image edges and reconstructs high-frequency details to preserve the face structure. The proposed technique to generate super-resolved features is usable in any face recognition method. We have used some state-of-the-art face recognition methods to evaluate the proposed method. The results showed a significant impact of the proposed method on the accuracy of face recognition of low resolution images.

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Sentiment analysis and emotion detection of post-COVID educational Tweets: Jordan case

Qaqish

Aranki

Etaiwi

2023

Soc. Netw. Anal. Min.

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Education evolved dramatically under Covid-19, and owing to the conditions, distant learning became mandatory. However, this has opened new realities to the educational business under the label of “Hybrid-Learning,” where educational institutions are still using online learning in addition to face-to-face learning, which has changed people’s lives and split their opinions and emotions. As a result, this study investigated the Jordanian community’s perspectives and feelings on the transition from pure face-to-face education to blended education by examining related tweets in the post-COVID era. Specifically, using NLP Emotion detection and Sentiment Analysis approaches, as well as deep learning models. As a result of analyzing the collected tweets, 18.75% of studied Jordanian’s community sample are dissatisfied (Anger and Hate), 21.25% are negative (Sad), 13% are Happy, and 24.50 percent are Neutral about it.

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Emotion Recognition Using Deep Learning in Pandemic with Real-time Email Alert

Cited by 3 publications

References 27 publications

Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

Empowering Face Recognition Methods using a GAN-based Single Image Super-Resolution Network

Sentiment analysis and emotion detection of post-COVID educational Tweets: Jordan case

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