{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T18:59:23Z","timestamp":1789498763617,"version":"build-2803163510"},"reference-count":36,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T00:00:00Z","timestamp":1731974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,12,31]]},"abstract":"<jats:p>In the realm of natural language processing, tasks like emotion recognition, irony detection, hate speech detection, offensive language identification, and stance detection are pivotal for understanding user-generated content. While several task-specific and multitask learning models have been proposed, there remains a need for a unified framework that can effectively address these tasks simultaneously. This research introduces a novel unified framework designed to tackle multiple NLP tasks concurrently, aiming to outperform existing task-specific and multitask models in terms of accuracy, F1-score, and AUC-ROC. We compared our proposed framework against several baseline models, including task-specific models like SVM, RF, LSTM, CNN, and BERT, as well as multitask learning frameworks such as Hard Parameter Sharing, Soft Parameter Sharing, Cross-stitch Networks, MMoE, and T5. The performance was evaluated across various tasks, and statistical significance was assessed using the Wilcoxon signed-rank test. Additionally, an ablation study was conducted to determine the contribution of individual components within our proposed method. The proposed framework consistently outperformed other models across all tasks. For instance, in emotion recognition, our model achieved an accuracy of 0.899, F1-score of 0.883, and AUC-ROC of 0.971, surpassing all baseline models. The Wilcoxon signed-rank test further confirmed the statistical superiority of our model over the baselines across all datasets.<\/jats:p>","DOI":"10.1145\/3682064","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T14:06:56Z","timestamp":1722262016000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["A Unified Framework for Analyzing Textual Context and Intent in Social Media"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5427-9460","authenticated-orcid":false,"given":"V. Jothi","family":"Prakash","sequence":"first","affiliation":[{"name":"Karpagam College of Engineering, Coimbatore, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5543-7547","authenticated-orcid":false,"given":"S. Arul Antran","family":"Vijay","sequence":"additional","affiliation":[{"name":"Karpagam College of Engineering, Coimbatore, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,19]]},"reference":[{"issue":"8","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3548772","article-title":"Deep learning in sentiment analysis: Recent architectures","volume":"55","author":"Abdullah Tariq","year":"2022","unstructured":"Tariq Abdullah and Ahmed Ahmet. 2022. Deep learning in sentiment analysis: Recent architectures. Comput. Surveys 55, 8 (2022), 1\u201337.","journal-title":"Comput. Surveys"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Francesco Barbieri Jose Camacho-Collados Leonardo Neves and Luis Espinosa-Anke. 2020. Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv:2010.12421. Retrieved from https:\/\/arxiv.org\/abs\/2010.12421","DOI":"10.18653\/v1\/2020.findings-emnlp.148"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.23071"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.54365\/adyumbd.1106981"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.01.021"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119446"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498511"},{"key":"e_1_3_1_9_2","article-title":"Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions","author":"Gandhi Ankita","year":"2022","unstructured":"Ankita Gandhi, Kinjal Adhvaryu, Soujanya Poria, Erik Cambria, and Amir Hussain. 2022. Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions. Information Fusion.","journal-title":"Information Fusion"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110182"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2022.110310"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/app13020903"},{"issue":"3","key":"e_1_3_1_13_2","first-page":"389","article-title":"Enhanced evolutionary feature selection and ensemble method for cardiovascular disease prediction","volume":"13","author":"Prakash V Jothi","year":"2021","unstructured":"V Jothi Prakash and NK Karthikeyan. 2021. Enhanced evolutionary feature selection and ensemble method for cardiovascular disease prediction. Interdisciplinary Sciences: Computational Life Sciences 13, 3 (2021), 389\u2013412.","journal-title":"Interdisciplinary Sciences: Computational Life Sciences"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1177\/0165551521990617"},{"issue":"10","key":"e_1_3_1_15_2","article-title":"Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning","volume":"8","author":"Leelawat Natt","year":"2022","unstructured":"Natt Leelawat, Sirawit Jariyapongpaiboon, Arnon Promjun, Samit Boonyarak, Kumpol Saengtabtim, Ampan Laosunthara, Alfan Kurnia Yudha, and Jing Tang. 2022. Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning. Heliyon 8, 10 (2022).","journal-title":"Heliyon"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301216"},{"key":"e_1_3_1_17_2","first-page":"1","article-title":"A RoBERTa based model for identifying the multi-modal informative tweets during disaster","author":"Madichetty Sreenivasulu","year":"2023","unstructured":"Sreenivasulu Madichetty and Sreekanth Madisetty. 2023. A RoBERTa based model for identifying the multi-modal informative tweets during disaster. Multimedia Tools and Applications (2023), 1\u201319.","journal-title":"Multimedia Tools and Applications"},{"key":"e_1_3_1_18_2","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/978-981-19-6581-4_21","volume-title":"Intelligent Systems and Applications: Select Proceedings of ICISA 2022","author":"Mann Suman","year":"2023","unstructured":"Suman Mann, Jyoti Arora, Mudita Bhatia, Ritika Sharma, and Rewangi Taragi. 2023. Twitter sentiment analysis using enhanced BERT. In Intelligent Systems and Applications: Select Proceedings of ICISA 2022. Springer, 263\u2013271."},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-022-00726-4"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10579-022-09582-8"},{"issue":"1","key":"e_1_3_1_21_2","doi-asserted-by":"crossref","first-page":"158","DOI":"10.5755\/j01.itc.51.1.30083","article-title":"Dual-layer deep ensemble techniques for classifying heart disease","volume":"51","author":"Prakash V Jothi","year":"2022","unstructured":"V Jothi Prakash and NK Karthikeyan. 2022. Dual-layer deep ensemble techniques for classifying heart disease. Information Technology and Control 51, 1 (2022), 158\u2013179.","journal-title":"Information Technology and Control"},{"issue":"3","key":"e_1_3_1_22_2","doi-asserted-by":"crossref","first-page":"457","DOI":"10.29207\/resti.v7i3.4892","article-title":"Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTa","volume":"7","author":"Putra Diaz Tiyasya","year":"2023","unstructured":"Diaz Tiyasya Putra, Erwin Budi Setiawan, et al. 2023. Sentiment Analysis on Social Media with Glove Using Combination CNN and RoBERTa. Journal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, 3 (2023), 457\u2013563.","journal-title":"Journal RESTI (Rekayasa Sistem dan Teknologi Informasi)"},{"key":"e_1_3_1_23_2","article-title":"A fine-tuned BERT-based transfer learning approach for text classification","author":"Qasim Rukhma","year":"2022","unstructured":"Rukhma Qasim, Waqas Haider Bangyal, Mohammed A Alqarni, and Abdulwahab Ali Almazroi. 2022. A fine-tuned BERT-based transfer learning approach for text classification. Journal of Healthcare Engineering.","journal-title":"Journal of Healthcare Engineering"},{"key":"e_1_3_1_24_2","unstructured":"Aneri Rana and Sonali Jha. 2022. Emotion based hate speech detection using multimodal learning. arXiv:2202.06218. Retrieved from https:\/\/arxiv.org\/abs\/2202.06218"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2012.02.005"},{"key":"e_1_3_1_26_2","first-page":"671","volume-title":"International Conference on Multi-disciplinary Trends in Artificial Intelligence","author":"Sampath Koyyalagunta Krishna","year":"2023","unstructured":"Koyyalagunta Krishna Sampath and M Supriya. 2023. Traffic Prediction in Indian Cities from Twitter Data Using Deep Learning and Word Embedding Models. In International Conference on Multi-disciplinary Trends in Artificial Intelligence. Springer, 671\u2013682."},{"key":"e_1_3_1_27_2","first-page":"1","article-title":"Ensemble-based domain adaptation on social media posts for irony detection","author":"Saroj Anita","year":"2023","unstructured":"Anita Saroj and Sukomal Pal. 2023. Ensemble-based domain adaptation on social media posts for irony detection. Multimedia Tools and Applications (2023), 1\u201320.","journal-title":"Multimedia Tools and Applications"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2021.12.052"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1111\/coin.12563"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-023-10235-4"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583860"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12145-023-00940-w"},{"key":"e_1_3_1_33_2","unstructured":"Abhishek Velankar Hrushikesh Patil Amol Gore Shubham Salunke and Raviraj Joshi. 2022. L3cube-mahahate: A tweet-based Marathi hate speech detection dataset and bert models. arXiv:2203.13778. Retrieved from https:\/\/arxiv.org\/abs\/2203.13778"},{"key":"e_1_3_1_34_2","first-page":"1663","volume-title":"Proceedings of the ACM Web Conference","author":"Wang Lingzhi","year":"2022","unstructured":"Lingzhi Wang, Jing Li, Xingshan Zeng, and Kam-Fai Wong. 2022. Successful new-entry prediction for multi-party online conversations via latent topics and discourse modeling. In Proceedings of the ACM Web Conference, 1663\u20131672."},{"key":"e_1_3_1_35_2","first-page":"3307","volume-title":"Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE)","author":"Wang Sinan","year":"2022","unstructured":"Sinan Wang, Yumeng Li, Hongyan Li, Tanchao Zhu, Zhao Li, and Wenwu Ou. 2022a. Multi-task learning with calibrated mixture of insightful experts. In Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 3307\u20133319."},{"key":"e_1_3_1_36_2","doi-asserted-by":"crossref","unstructured":"Shweta Yadav Jainish Chauhan Joy Prakash Sain Krishnaprasad Thirunarayan Amit Sheth and Jeremiah Schumm. 2020. Identifying depressive symptoms from tweets: Figurative language enabled multitask learning framework. arXiv:2011.06149. Retrieved from https:\/\/arxiv.org\/abs\/2011.06149","DOI":"10.18653\/v1\/2020.coling-main.61"},{"key":"e_1_3_1_37_2","unstructured":"Tianhua Zhang Hongyin Luo Yung-Sung Chuang Wei Fang Luc Gaitskell Thomas Hartvigsen Xixin Wu Danny Fox Helen Meng and James Glass. 2023. Interpretable unified language checking. arXiv:2304.03728. Retrieved from https:\/\/arxiv.org\/abs\/2304.03728"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3682064","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3682064","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:10:03Z","timestamp":1750295403000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3682064"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,19]]},"references-count":36,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12,31]]}},"alternative-id":["10.1145\/3682064"],"URL":"https:\/\/doi.org\/10.1145\/3682064","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,19]]},"assertion":[{"value":"2023-09-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-07-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}