Ta'lim fidoyilari
Pokiza Kakhkhorova
Nodir Egamberdiyev
10.5281/zenodo.19354899
Annotatsiya
Accurately measuring human emotions from textual data remains a significant challenge in Natural Language Processing (NLP) due to linguistic nuances like sarcasm, which often lead to misclassification. This paper presents a hybrid Emotion Measurement System (EMS) that utilizes NoSQL (MongoDB) for scalable data handling and a retrained RoBERTa model for irony detection. A primary focus is placed on the “Dissonance Score”—a novel parameter implemented to bridge the gap between static rule-based sentiment and ironic context. The research demonstrates that activating the Dissonance Score at a threshold of 0.5 significantly improves accuracy in cases where traditional models fail to recognize ironic intent
Kalit so'zlar:
Emotion Measurement System, NoSQL, RoBERTa, Sarcasm Detection,
Dissonance Score, Hybrid Systems.
Foydalanilgan adabiyotlar
Liu, Y., et al. “RoBERTa: A Robustly Optimized BERT Pretraining Approach.” arXiv preprint arXiv:1907.11692, 2019, 1-20 pages. 2. Babanejad, N., et al. “Affective and Contextual Embedding for Sarcasm Detection.” COLING, 2020, 2368-2379 pages. 3. Riloff, E., et al. “Sarcasm as Contrast between a Positive Sentiment and Negative Situation.” EMNLP, 2013, 704-714 pages. 4. Van Hee, C., et al. “SemEval-2018 Task 3: Irony Detection in English Tweets.” SemEval, 2018, 33-50 pages. 5. Van der Linde, I. “A comparison of sentiment analysis techniques in a parallel and distributed NoSQL environment.” M.Sc. Thesis, University of the Free State, 2020, 1-142 pages.
