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
Findings of the Sentiment Analysis of Dravidian Languages in Code-Mixed Text
Bharathi Raja Chakravarthi, Ruba Priyadharshini, Sajeetha Thavareesan, Dhivya Chinnappa, Durairaj Thenmozhi, Elizabeth Sherly, John P. McCrae, Adeep Hande, Rahul Ponnusamy, Shubhanker Banerjee, Charangan Vasantharajan
Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model
Hongjiang Jing, Zuchao Li, Hai Zhao, Shu Jiang
Contrastive Clustering: Toward Unsupervised Bias Reduction for Emotion and Sentiment Classification
Jared Mowery
Automatic evaluation of scientific abstracts through natural language processing
Lucas G. O. Lopes, Thales M. A. Vieira, William W. M. Lira
Improving usual Naive Bayes classifier performances with Neural Naive Bayes based models
Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski
End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis
Gerhard Johann Hagerer, David Szabo, Andreas Koch, Maria Luisa Ripoll Dominguez, Christian Widmer, Maximilian Wich, Hannah Danner, Georg Groh
A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining
Gerhard Johann Hagerer, Wing Sheung Leung, Qiaoxi Liu, Hannah Danner, Georg Groh