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
Evaluating Large Language Models Against Human Annotators in Latent Content Analysis: Sentiment, Political Leaning, Emotional Intensity, and Sarcasm
Ljubisa Bojic, Olga Zagovora, Asta Zelenkauskaite, Vuk Vukovic, Milan Cabarkapa, Selma Veseljević Jerkovic, Ana Jovančevic
Towards New Benchmark for AI Alignment & Sentiment Analysis in Socially Important Issues: A Comparative Study of Human and LLMs in the Context of AGI
Ljubisa Bojic, Dylan Seychell, Milan Cabarkapa
Distilling Fine-grained Sentiment Understanding from Large Language Models
Yice Zhang, Guangyu Xie, Hongling Xu, Kaiheng Hou, Jianzhu Bao, Qianlong Wang, Shiwei Chen, Ruifeng Xu
On the Applicability of Zero-Shot Cross-Lingual Transfer Learning for Sentiment Classification in Distant Language Pairs
Andre Rusli, Makoto Shishido
BanglishRev: A Large-Scale Bangla-English and Code-mixed Dataset of Product Reviews in E-Commerce
Mohammad Nazmush Shamael, Sabila Nawshin, Swakkhar Shatabda, Salekul Islam
SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum Algorithms and Neuro-Fuzzy Systems
Kshitij Dave, Nouhaila Innan, Bikash K. Behera, Zahid Mumtaz, Saif Al-Kuwari, Ahmed Farouk
Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with Large Language Models
Chengyan Wu, Bolei Ma, Zheyu Zhang, Ningyuan Deng, Yanqing He, Yun Xue