Artificial Immune Systems-Based Classification Model for Code-Mixed Social Media Data

•Artificial immune systems-based classification model is proposed.•Artificial immune systems are inspired by biological immune system.•Artificial immune systems are used to optimize the Long short-term memory model.•The artificial immune systems-based LSTM model is used to classify code-mixed data....

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Bibliographic Details
Published in:Ingénierie et recherche biomédicale 2022-04, Vol.43 (2), p.120-129
Main Authors: Shekhar, S., Sharma, D.K., Agarwal, D.K., Pathak, Y.
Format: Article
Language:English
Subjects:
Ambiguous words
Artificial immune systems
Biologically stimulated
Language identification
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Summary:•Artificial immune systems-based classification model is proposed.•Artificial immune systems are inspired by biological immune system.•Artificial immune systems are used to optimize the Long short-term memory model.•The artificial immune systems-based LSTM model is used to classify code-mixed data. The main focus of the paper is to propose an artificial immune systems-based classification model for code-mixed social media data. The artificial immune systems are computational models inspired by the biological immune system. In this paper, artificial immune systems are used to optimize the initial parameters of Long short-term memory (LSTM) model. The proposed artificial immune systems-based LSTM model is then used for the classification of code-mixed data. The classification of Hindi-English code-mixed data into Hindi, English, and ambiguous words is done. Popular word embedding features were used for the representation of each word. The word embedding features and character embedding features have been used. The proposed method helps in identifying the word context by extracting the intent of user for using the ambiguous word in code-mixed sentence. Extensive experiments reveal that the artificial immune systems-based classification model outperforms competitive models especially when there are some ambiguous words in the social media data.
ISSN:1959-0318
DOI:10.1016/j.irbm.2020.07.004