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
Dhoroni: Exploring Bengali Climate Change and Environmental Views with a Multi-Perspective News Dataset and Natural Language Processing
Azmine Toushik Wasi, Wahid Faisal, Taj Ahmad, Abdur Rahman, Mst Rafia Islam
Dynamic Adaptive Rank Space Exploration for Efficient Sentiment Analysis with Large Language Models
Hongcheng Ding, Fuzhen Hu, Xuanze Zhao, Zixiao Jiang, Shamsul Nahar Abdullah, Deshinta Arrova Dewi
Sentiment Analysis Based on RoBERTa for Amazon Review: An Empirical Study on Decision Making
Xinli Guo
You Shall Know a Tool by the Traces it Leaves: The Predictability of Sentiment Analysis Tools
Daniel Baumartz, Mevlüt Bagci, Alexander Henlein, Maxim Konca, Andy Lücking, Alexander Mehler
Utilizing Large Language Models for Event Deconstruction to Enhance Multimodal Aspect-Based Sentiment Analysis
Xiaoyong Huang, Heli Sun, Qunshu Gao, Wenjie Huang, Ruichen Cao
Towards Hybrid Intelligence in Journalism: Findings and Lessons Learnt from a Collaborative Analysis of Greek Political Rhetoric by ChatGPT and Humans
Thanasis Troboukis, Kelly Kiki, Antonis Galanopoulos, Pavlos Sermpezis, Stelios Karamanidis, Ilias Dimitriadis, Athena Vakali
Enhancing Sentiment Analysis with Collaborative AI: Architecture, Predictions, and Deployment Strategies
Chaofeng Zhang, Jia Hou, Xueting Tan, Caijuan Chen, Hiroshi Hashimoto