@inproceedings{jadon-etal-2025-spatio,
    title = "Spatio-Temporal Mechanism in Multilingual Sentiment Analysis",
    author = "Jadon, Adarsh Singh  and
      Tiwari, Vivek  and
      Swain, Chittaranjan  and
      Dewangan, Deepak Kumar",
    editor = "Das, Sudhansu Bala  and
      Mishra, Pruthwik  and
      Singh, Alok  and
      Muhammad, Shamsuddeen Hassan  and
      Ekbal, Asif  and
      Das, Uday Kumar",
    booktitle = "Proceedings of the Workshop on Beyond English: Natural Language Processing for all Languages in an Era of Large Language Models",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, BULGARIA",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.globalnlp-1.10/",
    pages = "82--89",
    abstract = "This study investigated the effectiveness of various models in deep learning in performing sentiment analysis on code-mixed Hinglish text, a hybrid language is widely used in digital telecommunication. Hinglish presents unique challenges due to its informal nature, frequent code-switching, and complex linguistic structure. This research leverages datasets from the HinGE, SemEval-2020 Task 9 {\&} E-Commerce Reviews, datasets, competition, and employ models such as RNN (LSTM), BERT-LSTM, CNN, and a proposed BiLSTM model with Data Augmentation."
}