Abstract
This research investigates the correlation between Sentiment and SEPSIS(SpEculation, oPinion, biaS, and twISt) characteristics in news sentences through an ablation study. Various Sentiment analysis models, including TextBlob, Vader, and RoBERTa, are examined to discern their impact on news sentences. Additionally, we explore the Logistic Regression(LR), Decision Trees(DT), Support Vector Machines(SVM) and Convolutional Neural Network (CNN) models for Septic sentence classification.- Anthology ID:
- 2023.icon-1.74
- Volume:
- Proceedings of the 20th International Conference on Natural Language Processing (ICON)
- Month:
- December
- Year:
- 2023
- Address:
- Goa University, Goa, India
- Editors:
- Jyoti D. Pawar, Sobha Lalitha Devi
- Venue:
- ICON
- SIG:
- SIGLEX
- Publisher:
- NLP Association of India (NLPAI)
- Note:
- Pages:
- 738–742
- Language:
- URL:
- https://aclanthology.org/2023.icon-1.74
- DOI:
- Cite (ACL):
- Soma Das, Sagarika Ghosh, and Sanjay Chatterji. 2023. Identifying Correlation between Sentiment Analysis and Septic News Sentences Classification Tasks. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 738–742, Goa University, Goa, India. NLP Association of India (NLPAI).
- Cite (Informal):
- Identifying Correlation between Sentiment Analysis and Septic News Sentences Classification Tasks (Das et al., ICON 2023)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2023.icon-1.74.pdf