Sentiment Analysis with more context: Sense-enhanced Sentiment Analysis
Organization
TartuNLP
Abstract
Contextualized word embeddings have been employed effectively across several tasks in Natural Language Processing. In this study, the word sense disambiguation (WSD) technique will be applied along with the contextualized word embeddings to the existing sentiment datasets to capture more semantic information which helps to understand the attitudes, opinions, and emotions behind the text.
Outcome: A novel sense-enhanced sentiment analysis model
Outcome: A novel sense-enhanced sentiment analysis model
Graduation Theses defence year
2022-2023
Supervisor
Somnath Banerjee, Rajesh Sharma
Spoken language (s)
Requirements for candidates
Level
Masters
Keywords
Application of contact
Name
Somnath Banerjee, Rajesh Sharma
Phone