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• W4225012186 abstract "Zigzag conversational patterns of contents in social media are often perceived as noisy or informal text. Unrestricted usage of vocabulary in social media communications complicates the processing of code-mixed text. This paper accentuates two major aspects of code mixed text: Offensive Language Identification and Sentiment Analysis for Malayalam-English code-mixed data set. The proffered framework addresses 3 key points apropos these tasks-dependencies among features created by embedding methods (Word2Vec and FastText), comparative analysis of deep learning algorithms (uni-/bi-directional models, hybrid models, and transformer approaches), relevance of selective translation and transliteration and hyper-parameter optimization-which ensued in F1-Scores (model's accuracy) of 0.76 for Forum for Information Retrieval Evaluation (FIRE) 2020 and 0.99 for European Chapter of the Association for Computational Linguistics (EACL) 2021 data sets. A detailed error analysis was also done to give meaningful insights. The submitted strategy turned in the best results among the benchmarked models dealing with Malayalam-English code-mixed messages and it serves as an important step towards societal good." @default.
• W4225012186 created "2022-04-29" @default.
• W4225012186 creator A5029900047 @default.
• W4225012186 creator A5059356303 @default.
• W4225012186 date "2022-04-26" @default.
• W4225012186 modified "2023-09-26" @default.
• W4225012186 title "Social media text analytics of Malayalam–English code-mixed using deep learning" @default.
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• W4225012186 doi "https://doi.org/10.1186/s40537-022-00594-3" @default.
• W4225012186 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35495077" @default.
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