@inproceedings{88155ad8ca6941f6b52a1c715bc0689b,
title = "Cooking Well, With Love, Is an Art: Transformers on Youtube Hinglish Data",
abstract = "Youtube gives a place for viewers to express their views on a variety of issues. This study focuses on opinion mining from a dataset containing comments from Nisha Madulika's and Kabita's Kitchen's YouTube culinary channels. The information includes Mix code comments in both English and a Hindi-English hybrid known as Hinglish. On both datasets, variations of the Bidirectional Encoder Representations from Transformers (BERT) have been trained. BERT outperformed the baseline trained on machine learning in terms of outcomes. The results show that the innovative BERT models (84.82\% and 88.37\%) are likely to outperform on Hinglish datasets with fine-tuning.",
keywords = "BERT, Classification, Cookery Channels, Hinglish, Machine Learning, Pretrained, Sentiment Analysis, Trans-formers",
author = "Sargam Yadav and Abhishek Kaushik and Shubham Sharma",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Computational Performance Evaluation, ComPE 2021 ; Conference date: 01-12-2021 Through 03-12-2021",
year = "2021",
doi = "10.1109/ComPE53109.2021.9752377",
language = "English",
series = "2021 International Conference on Computational Performance Evaluation, ComPE 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "836--841",
editor = "Sudip Paul and Verma, \{Jitendra Kumar\}",
booktitle = "2021 International Conference on Computational Performance Evaluation, ComPE 2021",
address = "United States",
}