% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").
%1 --- cited

@article{rajendram2022contextual,
  title={Contextual emotion detection on text using gaussian process and tree based classifiers},
  author={S Angel Deborah and Rajendram S Milton and TT Mirnalinee and S  Rajalakshmi},
  journal={Intelligent Data Analysis},
  volume={26},
  number={1},
  pages={119--132},
  year={2022},
  publisher={IOS Press}
}

@inproceedings{s-etal-2022-varsini,
    title = "{V}arsini{\_}and{\_}{K}irthanna@{D}ravidian{L}ang{T}ech-{ACL}2022-Emotional Analysis in {T}amil",
    author = "S, Varsini  and
      Rajan, Kirthanna  and
      S, Angel  and
      Sivanaiah, Rajalakshmi  and
      Rajendram, Sakaya Milton  and
      T T, Mirnalinee",
     booktitle = "Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.dravidianlangtech-1.26",
    doi = "10.18653/v1/2022.dravidianlangtech-1.26",
    pages = "165--169",
    abstract = "In this paper, we present our system for the task of Emotion analysis in Tamil. Over 3.96 million people use these platforms to send messages formed using texts, images, videos, audio or combinations of these to express their thoughts and feelings. Text communication on social media platforms is quite overwhelming due to its enormous quantity and simplicity. The data must be processed to understand the general feeling felt by the author. We present a lexicon-based approach for the extraction emotion in Tamil texts. We use dictionaries of words labelled with their respective emotions. The process of assigning an emotional label to each text, and then capture the main emotion expressed in it. Finally, the F1-score in the official test set is 0.0300 and our method ranks 5th.",
}


@inproceedings{angel2020contextual,
  title={Contextual Emotion Detection in Text Using Ensemble Learning},
  author={Angel Deborah, S and Rajalakshmi, S and Milton Rajendram, S and Mirnalinee, TT},
  booktitle={Emerging Trends in Computing and Expert Technology},
  pages={1179--1186},
  year={2020},
  organization={Springer}
}
@inproceedings{thakur2020current,
  title={Current state of Hinglish text sentiment analysis},
  author={Thakur, Varsha and Sahu, Roshani and Omer, Somya},
  booktitle={Proceedings of the International Conference on Innovative Computing \& Communications (ICICC)},
  year={2020}
}

%2 --- cited
@inproceedings{ravi2016sentiment,
  title={Sentiment classification of Hinglish text},
  author={Ravi, Kumar and Ravi, Vadlamani},
  booktitle={2016 3rd International Conference on Recent Advances in Information Technology (RAIT)},
  pages={641--645},
  year={2016},
  organization={IEEE}
}

%3 --- cited
@article{singh2021sentiment,
  title={Sentiment Analysis of Code-Mixed Social Media Text (Hinglish)},
  author={Singh, Gaurav},
  journal={arXiv preprint arXiv:2102.12149},
  year={2021}
}

%4 --- cited
@article{patwa2020semeval,
  title={Semeval-2020 task 9: Overview of sentiment analysis of code-mixed tweets},
  author={Patwa, Parth and Aguilar, Gustavo and Kar, Sudipta and Pandey, Suraj and Pykl, Srinivas and Gamb{\"a}ck, Bj{\"o}rn and Chakraborty, Tanmoy and Solorio, Thamar and Das, Amitava},
  journal={arXiv preprint arXiv:2008.04277},
  year={2020}
}

%5 --- cited
@article{srinivasan2023sentimental,
  title={Sentimental analysis from imbalanced code-mixed data using machine learning approaches},
  author={Srinivasan, R and Subalalitha, CN},
  journal={Distributed and Parallel Databases},
  volume={41},
  number={1},
  pages={37--52},
  year={2023},
  publisher={Springer}
}

%6 --- cited
@article{mundra2023hinglish,
  title={Hinglish HateBERT: Hate Speech Classification in Hinglish language},
  author={Mundra, Shikha and others},
  year={2023}
}

%7 --- cited
@inproceedings{gupta2021sentiment, 
  title={Sentiment Analysis of Hinglish Text and Sarcasm Detection},
  author={Gupta, Abhishek and Mishra, Abinash and Reddy, U Srinivasulu},
  booktitle={Conference Proceedings of ICDLAIR2019},
  pages={11--20},
  year={2021},
}  organization={Springer}

%8
@inproceedings{das2023sentiment,
  title={Sentiment Recognition of Hinglish Code Mixed Data using Deep Learning Models based Approach},
  author={Das, Shubham and Singh, Tanya},
  booktitle={2023 13th International Conference on Cloud Computing, Data Science \& Engineering (Confluence)},
  pages={265--269},
  year={2023},
  organization={IEEE}
}

%9 --- cited
@inproceedings{jadhav2022code,
  title={Code-Mixed Hinglish to English Language Translation Framework},
  author={Jadhav, Ishali and Kanade, Aditi and Waghmare, Vishesh and Chandok, Sahej Singh and Jarali, Ashwini},
  booktitle={2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)},
  pages={684--688},
  year={2022},
  organization={IEEE}
}

%10 --- cited
@inproceedings{sinha2005machine, 
  title={Machine translation of bi-lingual hindi-english (hinglish) text},
  author={Sinha, R Mahesh K and Thakur, Anil},
  booktitle={Proceedings of Machine Translation Summit X: Papers},
  pages={149--156},
  year={2005}
}

@article{survey,
author = {Ly, Cong Khanh},
year = {2022},
month = {12},
pages = {19-33},
title = {English as a Global Language: An Exploration of EFL Learners' Beliefs in Vietnam},
volume = {3},
journal = {International Journal of TESOL & Education},
doi = {10.54855/ijte.23312}
}



@inproceedings{nayak-joshi-2022-l3cube,
    title = "{L}3{C}ube-{H}ing{C}orpus and {H}ing{BERT}: A Code Mixed {H}indi-{E}nglish Dataset and {BERT} Language Models",
    author = "Nayak, Ravindra  and Joshi, Raviraj",
    booktitle = "Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.wildre-1.2",
    pages = "7--12",
}


@article{liu2019roberta,
  title={Roberta: A robustly optimized bert pretraining approach},
  author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin},
  journal={arXiv preprint arXiv:1907.11692},
  year={2019}
}


@article{ekman1992argument,
  title={An argument for basic emotions},
  author={Ekman, Paul},
  journal={Cognition \& emotion},
  volume={6},
  number={3-4},
  pages={169--200},
  year={1992},
  publisher={Taylor \& Francis}
}


@inproceedings{kumar-etal-2023-multilingual,
    title = "From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues",
    author = "Kumar, Shivani  and
      S, Ramaneswaran  and
      Akhtar, Md  and
      Chakraborty, Tanmoy",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.598",
    doi = "10.18653/v1/2023.emnlp-main.598",
    pages = "9638--9652",
    abstract = "Understanding emotions during conversation is a fundamental aspect of human communication, driving NLP research for Emotion Recognition in Conversation (ERC). While considerable research has focused on discerning emotions of individual speakers in monolingual dialogues, understanding the emotional dynamics in code-mixed conversations has received relatively less attention. This motivates our undertaking of ERC for code-mixed conversations in this study. Recognizing that emotional intelligence encompasses a comprehension of worldly knowledge, we propose an innovative approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions. To achieve this, we devise an efficient pipeline that extracts relevant commonsense from existing knowledge graphs based on the code-mixed input. Subsequently, we develop an advanced fusion technique that seamlessly combines the acquired commonsense information with the dialogue!
  representation obtained from a dedicated dialogue understanding module. Our comprehensive experimentation showcases the substantial performance improvement obtained through the systematic incorporation of commonsense in ERC. Both quantitative assessments and qualitative analyses further corroborate the validity of our hypothesis, reaffirming the pivotal role of commonsense integration in enhancing ERC.",
}


@inproceedings{kumar2024semeval,

    title = "SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)",

    author = "Kumar, Shivani  and

      Akhtar, Md Shad  and

      Cambria, Erik and

      Chakraborty, Tanmoy",

    booktitle = "Proceedings of the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics",

    year = "2024",

    publisher = "Association for Computational Linguistics",

    url = "https://arxiv.org/abs/2402.18944",

}