Fine Grained Opinion Analysis

Fine-grained opinion analysis is a subtask of natural language processing that aims to detect attitudes expressed in opinions, such as emotions, agreement, beliefs, or intentions, measure the intensity of these opinions, identify the holders of the opinions, i.e., the entities expressing the attitudes, and recognize the targets of the opinions, i.e., the entities or propositions towards which the attitudes are directed, and classify target-dependent attitudes. This task helps to gain a deeper understanding of the complex opinion structures in texts, enhancing the accuracy of sentiment analysis and user behavior prediction.