


@article{googlemachinetranslation,
  title={Hindi to {E}nglish Translation using {P}ython},
  author={Adep, Venugopa},
  journal={kaggle},
  year={n.d.},
  url={https://www.kaggle.com/code/adepvenugopal/hindi-to-english-translation-using-python}
}          
        

@article{mymodel,
  title={Emotion Classification with {SpaCy} v3 and Comet},
  author={Kömeçoğlu, Buluz, Başak},
  year={2023},
  url={https://heartbeat.comet.ml/emotion-classification-with-spacy-v3-comet-eaff310c0d7c}
}          

@article{additionaldata,
  title={Emotion Detection in Text using Natural Language Processing},
  author={Nikam, Sanket},
  year={n.d.},
  url={https://github.com/SannketNikam/Emotion-Detection-in-Text}
}          

@article{hinglish,
  title={The rise and rise of {H}inglish in {I}ndia},
  author={Chand, Vineeta},
  year={2016},
  url={https://theconversation.com/the-rise-and-rise-of-hinglish-in-india-53476}
}

@article{SASIDHAR20201346,
title = {Emotion Detection in {H}inglish({H}indi+{E}nglish) Code-Mixed Social Media Text},
journal = {Procedia Computer Science},
volume = {171},
pages = {1346-1352},
year = {2020},
note = {Third International Conference on Computing and Network Communications (CoCoNet'19)},
issn = {1877-0509},
doi = {https://doi.org/10.1016/j.procs.2020.04.144},
url = {https://www.sciencedirect.com/science/article/pii/S1877050920311236},
author = {T Tulasi Sasidhar and Premjith B and Soman K P},
keywords = {Code-Mixed Text, Emotion Detection, ID-Convolution Neural Network, Word2Vec, Long Short-Term Memory},
abstract = {Human communication is often embedded with emotion and it can be expressed via different mediums like vocal interaction, texts, non-verbal communication like facial expressions and gestures. Even though textual communication is a more common way of interaction the rapid utilization of social media has taken it to another level. Social media open an easy way for people to express their emotions. So people around the world utilize this opportunity and express themselves in social media platforms through texts over various subjects. People make use of these platforms to exhibit their like or dislike towards something, how they felt about a situation, their reaction to a government decision and so on. Hence, understanding the emotion expressed in such social media texts has a significant number of applications emphasizing the need to detect it. The human brain is quite intelligent to sense such kind of emotion associated with a text but for a machine to gain such perception is quite difficult. In Natural Language Processing, emotion recognition and classification is a commonly researched task where a model can detect these type of emotions. It is quite challenging when it comes to Indian Languages due to the lack of data, as well as being a multilingual society people tend to use code-mixed pattern in social media. The lack of annotated corpus in the Hindi-English code-mixed domain and unavailability of the standard model to classify, left this area of research still an exploring region. In this paper, to analyze such data, we created a dataset of 12000 Hindi-English code-mixed texts collected from various sources and annotated them with emotions Happy, Sad and Anger. In our work, a pretrained bilingual model is used to generate feature vectors and deep neural networks are employed as classification models. It is observed that with the selected features, CNN-BiLSTM gave better performance compared to other experimented models with 83.21% classification accuracy.}
}


@inproceedings{semrel2024task,   
title="{S}em{E}val-2024 {T}ask 10:  {E}motion {D}iscovery and {R}easoning its {F}lip in {C}onversation ({EDiReF})",  
author="Shivani Kumar and Md Shad Akhtar and Tanmoy Chakraborty and Erik Cambria",   
booktitle="Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",  
 year=2024
}


@article{wadhawan2021towards,
  title={Towards emotion recognition in Hindi-English code-mixed data: A transformer based approach},
  author={Wadhawan, Anshul and Aggarwal, Akshita},
  journal={arXiv preprint arXiv:2102.09943},
  year={2021}
}


@Article{bdcc3030037,
AUTHOR = {Kaur, Gagandeep and Kaushik, Abhishek and Sharma, Shubham},
TITLE = {Cooking Is Creating Emotion: A Study on Hinglish Sentiments of Youtube Cookery Channels Using Semi-Supervised Approach},
JOURNAL = {Big Data and Cognitive Computing},
VOLUME = {3},
YEAR = {2019},
NUMBER = {3},
ARTICLE-NUMBER = {37},
URL = {https://www.mdpi.com/2504-2289/3/3/37},
ISSN = {2504-2289},
ABSTRACT = {The success of Youtube has attracted a lot of users, which results in an increase of the number of comments present on Youtube channels. By analyzing those comments we could provide insight to the Youtubers that would help them to deliver better quality. Youtube is very popular in India. A majority of the population in India speak and write a mixture of two languages known as Hinglish for casual communication on social media. Our study focuses on the sentiment analysis of Hinglish comments on cookery channels. The unsupervised learning technique DBSCAN was employed in our work to find the different patterns in the comments data. We have modelled and evaluated both parametric and non-parametric learning algorithms. Logistic regression with the term frequency vectorizer gave 74.01% accuracy in Nisha Madulika&rsquo;s dataset and 75.37% accuracy in Kabita&rsquo;s Kitchen dataset. Each classifier is statistically tested in our study.},
DOI = {10.3390/bdcc3030037}
}


@inproceedings{semeval2024task10,

    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",

}


@inproceedings{task10dataset,
    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.",
}