Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text
Abstract The paper describes the usage of self-learning Hierarchical LSTM technique for classifying hatred and trolling contents in social media code-mixed data. The Hierarchical LSTM-based learning is a novel learning architecture inspired from the neural learning models. The proposed HLSTM model i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2021-08-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-021-00487-7 |
| _version_ | 1797806456476532736 |
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| author | Shashi Shekhar Hitendra Garg Rohit Agrawal Shivendra Shivani Bhisham Sharma |
| author_facet | Shashi Shekhar Hitendra Garg Rohit Agrawal Shivendra Shivani Bhisham Sharma |
| author_sort | Shashi Shekhar |
| collection | DOAJ |
| description | Abstract The paper describes the usage of self-learning Hierarchical LSTM technique for classifying hatred and trolling contents in social media code-mixed data. The Hierarchical LSTM-based learning is a novel learning architecture inspired from the neural learning models. The proposed HLSTM model is trained to identify the hatred and trolling words available in social media contents. The proposed HLSTM systems model is equipped with self-learning and predicting mechanism for annotating hatred words in transliteration domain. The Hindi–English data are ordered into Hindi, English, and hatred labels for classification. The mechanism of word embedding and character-embedding features are used here for word representation in the sentence to detect hatred words. The method developed based on HLSTM model helps in recognizing the hatred word context by mining the intention of the user for using that word in the sentence. Wide experiments suggests that the HLSTM-based classification model gives the accuracy of 97.49% when evaluated against the standard parameters like BLSTM, CRF, LR, SVM, Random Forest and Decision Tree models especially when there are some hatred and trolling words in the social media data. |
| first_indexed | 2024-03-13T06:07:36Z |
| format | Article |
| id | doaj.art-a708268bd7c74a1a8d24becdde9debfd |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-03-13T06:07:36Z |
| publishDate | 2021-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-a708268bd7c74a1a8d24becdde9debfd2023-06-11T11:29:13ZengSpringerComplex & Intelligent Systems2199-45362198-60532021-08-01932813282610.1007/s40747-021-00487-7Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media textShashi Shekhar0Hitendra Garg1Rohit Agrawal2Shivendra Shivani3Bhisham Sharma4Department of Computer Engineering and Applications, GLA UniversityDepartment of Computer Engineering and Applications, GLA UniversityDepartment of Computer Engineering and Applications, GLA UniversityDepartment of Computer Science & Engineering, Thapar UniversityChitkara University School of Engineering and Technology, Chitkara UniversityAbstract The paper describes the usage of self-learning Hierarchical LSTM technique for classifying hatred and trolling contents in social media code-mixed data. The Hierarchical LSTM-based learning is a novel learning architecture inspired from the neural learning models. The proposed HLSTM model is trained to identify the hatred and trolling words available in social media contents. The proposed HLSTM systems model is equipped with self-learning and predicting mechanism for annotating hatred words in transliteration domain. The Hindi–English data are ordered into Hindi, English, and hatred labels for classification. The mechanism of word embedding and character-embedding features are used here for word representation in the sentence to detect hatred words. The method developed based on HLSTM model helps in recognizing the hatred word context by mining the intention of the user for using that word in the sentence. Wide experiments suggests that the HLSTM-based classification model gives the accuracy of 97.49% when evaluated against the standard parameters like BLSTM, CRF, LR, SVM, Random Forest and Decision Tree models especially when there are some hatred and trolling words in the social media data.https://doi.org/10.1007/s40747-021-00487-7Hatred detectionTrollingSocial mediaHLSTMEmbeddingTransliteration |
| spellingShingle | Shashi Shekhar Hitendra Garg Rohit Agrawal Shivendra Shivani Bhisham Sharma Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text Complex & Intelligent Systems Hatred detection Trolling Social media HLSTM Embedding Transliteration |
| title | Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text |
| title_full | Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text |
| title_fullStr | Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text |
| title_full_unstemmed | Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text |
| title_short | Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text |
| title_sort | hatred and trolling detection transliteration framework using hierarchical lstm in code mixed social media text |
| topic | Hatred detection Trolling Social media HLSTM Embedding Transliteration |
| url | https://doi.org/10.1007/s40747-021-00487-7 |
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