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 Cross-Validation Study of Turkish Sentiment Analysis Datasets and Tools
Şevval Çakıcı, Dilara Karaduman, Mehmet Akif Çırlan, Ali Hürriyetoğlu
Depression detection from Social Media Bangla Text Using Recurrent Neural Networks
Sultan Ahmed, Salman Rakin, Mohammad Washeef Ibn Waliur, Nuzhat Binte Islam, Billal Hossain, Md. Mostofa Akbar
Were You Helpful -- Predicting Helpful Votes from Amazon Reviews
Emin Kirimlioglu, Harrison Kung, Dominic Orlando
A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis
Changzhi Zhou, Dandan Song, Yuhang Tian, Zhijing Wu, Hao Wang, Xinyu Zhang, Jun Yang, Ziyi Yang, Shuhao Zhang
Understanding Student Sentiment on Mental Health Support in Colleges Using Large Language Models
Palak Sood, Chengyang He, Divyanshu Gupta, Yue Ning, Ping Wang
MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis
Yingjie Zhou, Zicheng Zhang, Jiezhang Cao, Jun Jia, Yanwei Jiang, Farong Wen, Xiaohong Liu, Xiongkuo Min, Guangtao Zhai