Identification of sarcasm in texts for sentimental analysis

Rаhhimov , Khаsаnboy, Рахимов , Хасанбой, Rаximov , Xаsаnboy

Хорижий лингвистика ва лингводидактика · 2024-yil

Annotatsiya

Humanity has discovered various ways to express emotions. Depending on the context of speech, these emotions are sometimes accompanied by sarcasm, particularly when expressing intense feelings. Over the past few decades, social media platforms such as Facebook, Instagram, TikTok, Twitter, and YouTube have become popular tools for people to share such strong emotions and personal thoughts with wide audiences. Through techniques like sentiment analysis, this data can be valuable in various fields, including business, marketing, production, behavioral analysis, and public management during ecological or biological crises, as well as in times of war. Most current research treats sentiment and sarcasm classification as two separate tasks, approaching each as an independent text classification problem. In recent years, studies using deep learning algorithms have significantly improved the effectiveness of these independent classifiers. However, one of the main challenges these approaches face is their inability to accurately classify sarcastic statements as negative. Taking this into account, we argue that recognizing sarcasm enhances sentiment classification, and vice versa. In this work, we demonstrate that these two tasks are interrelated. This paper proposes a multi-task learning framework that leverages deep neural networks to model this interrelation, aiming to improve the overall effectiveness of sentiment analysis.

Maqola ma’lumotlari
MualliflarRаhhimov , Khаsаnboy, Рахимов , Хасанбой, Rаximov , Xаsаnboy
JurnalХорижий лингвистика ва лингводидактика
NashriyotСамарқанд давлат чет тиллар институти
ISSN0000-0003, 4295-8199
Nashr sanasi2024-10-25
Jild2
Son4/S
Betlar95-104
TilIngliz
Ilmiy sohaPedagogika
DOI10.47689/2181-3701-vol2-iss4/s-pp95-104
Indeksga qo‘shilgan2026-08-26

DOI: 10.47689/2181-3701-vol2-iss4/s-pp95-104 · Maqolaning asl sahifasi

Kalit so‘zlar

sentiment analizi, ijtimoiy media platformalari, NLP, kinoya(sarcazm), deep learning algoritm, multi-task learning, polaritet, tokenizatsiya, анализ тональности, платформы социальных сетей, NLP, сарказм, алгоритмы глубокого обучения, обучение с несколькими задачами, полярность, токенизация, sentiment analysis, social media platforms, NLP, sarcasm, deep learning algorithm, multi-task learning, polarity, tokenization

Ilmiy soha

Pedagogika

Iqtibos keltirish

APARаhhimov , Khаsаnboy, Рахимов , Хасанбой, & Rаximov , Xаsаnboy (2024). Identification of sarcasm in texts for sentimental analysis. Хорижий лингвистика ва лингводидактика, 2(4/S), 95-104. https://doi.org/10.47689/2181-3701-vol2-iss4/s-pp95-104
GOST (OAK)Rаhhimov , Khаsаnboy, Рахимов , Хасанбой, Rаximov , Xаsаnboy Identification of sarcasm in texts for sentimental analysis // Хорижий лингвистика ва лингводидактика. – 2024. – T. 2, № 4/S. – P. 95-104. – DOI: 10.47689/2181-3701-vol2-iss4/s-pp95-104.
MLARаhhimov , Khаsаnboy, Рахимов , Хасанбой, and Rаximov , Xаsаnboy. “Identification of sarcasm in texts for sentimental analysis.” Хорижий лингвистика ва лингводидактика, 2(4/S), 95-104, 2024. https://doi.org/10.47689/2181-3701-vol2-iss4/s-pp95-104
ChicagoRаhhimov , Khаsаnboy, Рахимов , Хасанбой, and Rаximov , Xаsаnboy. “Identification of sarcasm in texts for sentimental analysis.” Хорижий лингвистика ва лингводидактика, 2(4/S), 95-104 (2024). https://doi.org/10.47689/2181-3701-vol2-iss4/s-pp95-104
HarvardRаhhimov , Khаsаnboy, Рахимов , Хасанбой, and Rаximov , Xаsаnboy (2024) ‘Identification of sarcasm in texts for sentimental analysis’, Хорижий лингвистика ва лингводидактика, 2(4/S), 95-104. https://doi.org/10.47689/2181-3701-vol2-iss4/s-pp95-104
BibTeX
@article{rhhimovkhsnboy2024,
  title = {Identification of sarcasm in texts for sentimental analysis},
  author = {Rаhhimov , Khаsаnboy and Рахимов , Хасанбой and Rаximov , Xаsаnboy},
  journal = {Хорижий лингвистика ва лингводидактика},
  year = {2024},
  volume = {2},
  number = {4/S},
  pages = {95-104},
  doi = {10.47689/2181-3701-vol2-iss4/s-pp95-104},
  url = {https://doi.org/10.47689/2181-3701-vol2-iss4/s-pp95-104}
}

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