Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model
With social media’s dominating role in the socio-political landscape, several existing and new forms of racism took place on social media. Racism has emerged on social media in different forms, both hidden and open, hidden with the use of memes and open as the racist remarks using fake id...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9684891/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850209084644524032 |
|---|---|
| author | Ernesto Lee Furqan Rustam Patrick Bernard Washington Fatima El Barakaz Wajdi Aljedaani Imran Ashraf |
| author_facet | Ernesto Lee Furqan Rustam Patrick Bernard Washington Fatima El Barakaz Wajdi Aljedaani Imran Ashraf |
| author_sort | Ernesto Lee |
| collection | DOAJ |
| description | With social media’s dominating role in the socio-political landscape, several existing and new forms of racism took place on social media. Racism has emerged on social media in different forms, both hidden and open, hidden with the use of memes and open as the racist remarks using fake identities to incite hatred, violence, and social instability. Although often associated with ethnicity, racism is now thriving based on color, origin, language, cultures, and most importantly religion. Social media opinions and remarks provocating racial differences have been regarded as a serious threat to social, political, and cultural stability and have threatened the peace of different countries. Consequently, social media being the leading source of racist opinions dissemination should be monitored and racism remarks should be detected and blocked timely. This study aims at detecting Tweets that contain racist text by performing the sentiment analysis of Tweets. Owing to the superior performance of deep learning, a stacked ensemble deep learning model is assembled by combining gated recurrent unit (GRU), convolutional neural networks (CNN), and recurrent neural networks RNN, called, Gated Convolutional Recurrent- Neural Networks (GCR-NN). GRU is on the top in the GCR-NN model to extract the suitable and prominent features from raw text, CNN extracts important features for RNN to make accurate predictions. Obviously, several experiments are conducted to investigate and analyze the performance of the proposed GCR-NN within the scope of machine learning and deep learning models indicating the superior performance of GCR-NN with increased 0.98 accuracy. The proposed GCR-NN model can detect 97% of the tweets that contain racist comments. |
| format | Article |
| id | doaj-art-29c8dfe9671c4aa7a4feff4fcf8dffb8 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-29c8dfe9671c4aa7a4feff4fcf8dffb82025-08-20T02:10:06ZengIEEEIEEE Access2169-35362022-01-01109717972810.1109/ACCESS.2022.31442669684891Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN ModelErnesto Lee0https://orcid.org/0000-0002-1209-8565Furqan Rustam1https://orcid.org/0000-0001-8403-1047Patrick Bernard Washington2https://orcid.org/0000-0002-3596-9167Fatima El Barakaz3Wajdi Aljedaani4Imran Ashraf5https://orcid.org/0000-0002-8271-6496Department of Computer Science, Broward College, Broward County, FL, USADepartment of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Punjab, PakistanDivision of Business Administration and Economics, Morehouse College, Atlanta, GA, USADepartment of Computer Science, Faculty of Sciences, Chouaib Doukkali University, El Jadida, MoroccoDepartment of Computer Science and Engineering, University of North Texas, Denton, TX, USADepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaWith social media’s dominating role in the socio-political landscape, several existing and new forms of racism took place on social media. Racism has emerged on social media in different forms, both hidden and open, hidden with the use of memes and open as the racist remarks using fake identities to incite hatred, violence, and social instability. Although often associated with ethnicity, racism is now thriving based on color, origin, language, cultures, and most importantly religion. Social media opinions and remarks provocating racial differences have been regarded as a serious threat to social, political, and cultural stability and have threatened the peace of different countries. Consequently, social media being the leading source of racist opinions dissemination should be monitored and racism remarks should be detected and blocked timely. This study aims at detecting Tweets that contain racist text by performing the sentiment analysis of Tweets. Owing to the superior performance of deep learning, a stacked ensemble deep learning model is assembled by combining gated recurrent unit (GRU), convolutional neural networks (CNN), and recurrent neural networks RNN, called, Gated Convolutional Recurrent- Neural Networks (GCR-NN). GRU is on the top in the GCR-NN model to extract the suitable and prominent features from raw text, CNN extracts important features for RNN to make accurate predictions. Obviously, several experiments are conducted to investigate and analyze the performance of the proposed GCR-NN within the scope of machine learning and deep learning models indicating the superior performance of GCR-NN with increased 0.98 accuracy. The proposed GCR-NN model can detect 97% of the tweets that contain racist comments.https://ieeexplore.ieee.org/document/9684891/Racismsocial mediaonline abuseTwitterdeep learning |
| spellingShingle | Ernesto Lee Furqan Rustam Patrick Bernard Washington Fatima El Barakaz Wajdi Aljedaani Imran Ashraf Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model IEEE Access Racism social media online abuse deep learning |
| title | Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model |
| title_full | Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model |
| title_fullStr | Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model |
| title_full_unstemmed | Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model |
| title_short | Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model |
| title_sort | racism detection by analyzing differential opinions through sentiment analysis of tweets using stacked ensemble gcr nn model |
| topic | Racism social media online abuse deep learning |
| url | https://ieeexplore.ieee.org/document/9684891/ |
| work_keys_str_mv | AT ernestolee racismdetectionbyanalyzingdifferentialopinionsthroughsentimentanalysisoftweetsusingstackedensemblegcrnnmodel AT furqanrustam racismdetectionbyanalyzingdifferentialopinionsthroughsentimentanalysisoftweetsusingstackedensemblegcrnnmodel AT patrickbernardwashington racismdetectionbyanalyzingdifferentialopinionsthroughsentimentanalysisoftweetsusingstackedensemblegcrnnmodel AT fatimaelbarakaz racismdetectionbyanalyzingdifferentialopinionsthroughsentimentanalysisoftweetsusingstackedensemblegcrnnmodel AT wajdialjedaani racismdetectionbyanalyzingdifferentialopinionsthroughsentimentanalysisoftweetsusingstackedensemblegcrnnmodel AT imranashraf racismdetectionbyanalyzingdifferentialopinionsthroughsentimentanalysisoftweetsusingstackedensemblegcrnnmodel |