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
Finding fake reviews in e-commerce platforms by using hybrid algorithms
Mathivanan Periasamy, Rohith Mahadevan, Bagiya Lakshmi S, Raja CSP Raman, Hasan Kumar S, Jasper Jessiman
"Hey..! This medicine made me sick": Sentiment Analysis of User-Generated Drug Reviews using Machine Learning Techniques
Abhiram B. Nair, Abhinand K., Anamika U., Denil Tom Jaison, Ajitha V., V. S. Anoop
BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights
Enmin Zhu, Jerome Yen
Sentiment Analysis of Citations in Scientific Articles Using ChatGPT: Identifying Potential Biases and Conflicts of Interest
Walid Hariri
M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets
Gaurish Thakkar, Sherzod Hakimov, Marko Tadić