Abdelwahed Motwakel^1,*, Hala J. Alshahrani², Jaber S. Alzahrani³, Ayman Yafoz⁴, Heba Mohsen⁵, Ishfaq Yaseen¹, Amgad Atta Abdelmageed¹, Mohamed I. Eldesouki⁶

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2741-2757, 2023, DOI:10.32604/csse.2023.034721

Abstract Currently, individuals use online social media, namely Facebook or Twitter, for sharing their thoughts and emotions. Detection of emotions on social networking sites’ finds useful in several applications in social welfare, commerce, public health, and so on. Emotion is expressed in several means, like facial and speech expressions, gestures, and written text. Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning (DL) and natural language processing (NLP) domains. This article proposes a Deer Hunting Optimization with Deep Belief Network Enabled Emotion Classification (DHODBN-EC) on English Twitter Data in this study. The presented… More >

Ibrahim M. Alwayle¹, Badriyya B. Al-onazi², Jaber S. Alzahrani³, Khaled M. Alalayah¹, Khadija M. Alaidarous¹, Ibrahim Abdulrab Ahmed⁴, Mahmoud Othman⁵, Abdelwahed Motwakel^6,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3423-3438, 2023, DOI:10.32604/csse.2023.033834

Abstract Arabic is one of the most spoken languages across the globe. However, there are fewer studies concerning Sentiment Analysis (SA) in Arabic. In recent years, the detected sentiments and emotions expressed in tweets have received significant interest. The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language. Two common models are available: Machine Learning and lexicon-based approaches to address emotion classification problems. With this motivation, the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition… More >

Xiaorui Zhang^1,2,3,*, Chunlin Yuan¹, Wei Sun^3,4, Sunil Kumar Jha⁵

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4273-4291, 2023, DOI:10.32604/cmc.2023.036975

Abstract The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the image. Studies have shown that certain local regions are more likely to inspire an emotional response than the whole image. However, existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the dataset. Therefore, this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature aggregation. To perform more accurate emotional region prediction, this study designs a multilayer attentional… More >

K. Anuratha^1,*, M. Parvathy²

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3005-3021, 2023, DOI:10.32604/csse.2023.031553

Abstract The COVID-19 pandemic has become one of the severe diseases in recent years. As it majorly affects the common livelihood of people across the universe, it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak. The public opinions are been shared enormously in microblogging media like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics, sports, entertainment etc., This work presents a combination of Intensity Based Emotion Classification Convolution Neural Network… More >

Chinu Singla¹, Fahd N. Al-Wesabi^2,3, Yash Singh Pathania¹, Badria Sulaiman Alfurhood⁴, Anwer Mustafa Hilal^5,*, Mohammed Rizwanullah⁵, Manar Ahmed Hamza⁵, Mohammad Mahzari⁶

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6365-6380, 2022, DOI:10.32604/cmc.2022.020480

Abstract The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average man. Analyzing this data can be critical for any organization. Sentiments are often expressed with different intensity and topics which can provide great insight into how something affects society. Sentiment analysis in Twitter mitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in twitter. Resources used for analyzing tweet emotions are also briefly presented in literature survey section.… More >

Hyun Yoo¹, Ji-Won Baek², Kyungyong Chung^3,*

Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 319-334, 2021, DOI:10.32604/iasc.2021.018115

Abstract With the convergence and development of the Internet of things (IoT) and artificial intelligence, closed-circuit television, wearable devices, and artificial neural networks have been combined and applied to crime prevention and follow-up measures against crimes. However, these IoT devices have various limitations based on the physical environment and face the fundamental problem of privacy violations. In this study, voice data are collected and emotions are classified based on an acoustic sensor that is free of privacy violations and is not sensitive to changes in external environments, to overcome these limitations. For the classification of emotions in the voice, the data… More >

Muhammad Zubair Asghar¹, Fazli Subhan², Muhammad Imran¹, Fazal Masud Kundi¹, Adil Khan³, Shahboddin Shamshirband^{4, 5, *}, Amir Mosavi^{6, 7, 8}, Peter Csiba⁸, Annamaria R. Varkonyi Koczy⁸

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1093-1118, 2020, DOI:10.32604/cmc.2020.07709

Abstract Emotion detection from the text is a challenging problem in the text analytics. The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine… More >