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
BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis
Sadia Alam, Md Farhan Ishmam, Navid Hasin Alvee, Md Shahnewaz Siddique, Md Azam Hossain, Abu Raihan Mostofa Kamal
Quantifying the Effectiveness of Student Organization Activities using Natural Language Processing
Lyberius Ennio F. Taruc, Arvin R. De La Cruz
Dynamic Adaptive Optimization for Effective Sentiment Analysis Fine-Tuning on Large Language Models
Hongcheng Ding, Xuanze Zhao, Shamsul Nahar Abdullah, Deshinta Arrova Dewi, Zixiao Jiang, Xiangyu Shi
A Deep Features-Based Approach Using Modified ResNet50 and Gradient Boosting for Visual Sentiments Classification
Muhammad Arslan, Muhammad Mubeen, Arslan Akram, Saadullah Farooq Abbasi, Muhammad Salman Ali, Muhammad Usman Tariq