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
RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers
Pratinav Seth, Rashi Goel, Komal Mathur, Swetha Vemulapalli
MMTF-DES: A Fusion of Multimodal Transformer Models for Desire, Emotion, and Sentiment Analysis of Social Media Data
Abdul Aziz, Nihad Karim Chowdhury, Muhammad Ashad Kabir, Abu Nowshed Chy, Md. Jawad Siddique
Cache me if you Can: an Online Cost-aware Teacher-Student framework to Reduce the Calls to Large Language Models
Ilias Stogiannidis, Stavros Vassos, Prodromos Malakasiotis, Ion Androutsopoulos
Enhancing Zero-Shot Crypto Sentiment with Fine-tuned Language Model and Prompt Engineering
Rahman S M Wahidur, Ishmam Tashdeed, Manjit Kaur, Heung-No-Lee
The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis
Pranav Narayanan Venkit, Mukund Srinath, Sanjana Gautam, Saranya Venkatraman, Vipul Gupta, Rebecca J. Passonneau, Shomir Wilson
On the use of Vision-Language models for Visual Sentiment Analysis: a study on CLIP
Cristina Bustos, Carles Civit, Brian Du, Albert Sole-Ribalta, Agata Lapedriza