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
Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic
António Câmara, Nina Taneja, Tamjeed Azad, Emily Allaway, Richard Zemel
Twitter Dataset on the Russo-Ukrainian War
Alexander Shevtsov, Christos Tzagkarakis, Despoina Antonakaki, Polyvios Pratikakis, Sotiris Ioannidis
Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets
Narayana Darapaneni, Anwesh Reddy Paduri, Himank Sharma, Milind Manjrekar, Nutan Hindlekar, Pranali Bhagat, Usha Aiyer, Yogesh Agarwal