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
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
A Temporal Psycholinguistics Approach to Identity Resolution of Social Media Users
Md Touhidul Islam
Monetizing Currency Pair Sentiments through LLM Explainability
Lior Limonad, Fabiana Fournier, Juan Manuel Vera Díaz, Inna Skarbovsky, Shlomit Gur, Raquel Lazcano
Sentiment Analysis of Lithuanian Online Reviews Using Large Language Models
Brigita Vileikytė, Mantas Lukoševičius, Lukas Stankevičius
Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach
Rian Dolphin, Joe Dursun, Jonathan Chow, Jarrett Blankenship, Katie Adams, Quinton Pike
Link Polarity Prediction from Sparse and Noisy Labels via Multiscale Social Balance
Marco Minici, Federico Cinus, Francesco Bonchi, Giuseppe Manco
ZZU-NLP at SIGHAN-2024 dimABSA Task: Aspect-Based Sentiment Analysis with Coarse-to-Fine In-context Learning
Senbin Zhu, Hanjie Zhao, Xingren Wang, Shanhong Liu, Yuxiang Jia, Hongying Zan