Sentiment Analysis
Sentiment analysis aims to automatically determine the emotional tone expressed in text, aiming to understand opinions and attitudes. Current research heavily focuses on leveraging large language models (LLMs) like BERT and its variants, along with other architectures such as graph neural networks, to improve accuracy and efficiency, particularly in multimodal settings and low-resource languages. This field is crucial for various applications, including market research, social media monitoring, and understanding public opinion, driving advancements in natural language processing and impacting decision-making across numerous sectors.
Papers
A Semi-supervised Approach for a Better Translation of Sentiment in Dialectical Arabic UGT
Hadeel Saadany, Constantin Orasan, Emad Mohamed, Ashraf Tantawy
Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals
Maarten De Raedt, Fréderic Godin, Chris Develder, Thomas Demeester
Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment Approach
Federico D'Amario, Milos Ciganovic
Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccines
Anmol Bansal, Arjun Choudhry, Anubhav Sharma, Seba Susan