User Sentiment
User sentiment analysis focuses on automatically identifying and interpreting the emotional tone expressed in text or multimedia data, aiming to understand user opinions and preferences. Current research emphasizes the use of large language models (LLMs) like GPT-4 and others, along with techniques like sentiment lexicons and multimodal analysis incorporating images and emojis, to improve accuracy and address challenges such as sarcasm and nuanced language. This field is crucial for various applications, including marketing, product development, political analysis, and mental health monitoring, providing valuable insights into human behavior and societal trends.
Papers
Self-training Strategies for Sentiment Analysis: An Empirical Study
Haochen Liu, Sai Krishna Rallabandi, Yijing Wu, Parag Pravin Dakle, Preethi Raghavan
Exploring Embeddings for Measuring Text Relatedness: Unveiling Sentiments and Relationships in Online Comments
Anthony Olakangil, Cindy Wang, Justin Nguyen, Qunbo Zhou, Kaavya Jethwa, Jason Li, Aryan Narendra, Nishk Patel, Arjun Rajaram