Fine-Grained Opinion Analysis

3 papers with code • 1 benchmarks • 1 datasets

Fine-Grained Opinion Analysis aims to: (i) detect opinion expressions that convey attitudes such as sentiments, agreements, beliefs, or intentions, (ii) measure their intensity, (iii) identify their holders i.e. entities that express an attitude, (iv) identify their targets i.e. entities or propositions at which the attitude is directed, and (v) classify their target-dependent attitude.

( Image credit: SRL4ORL )

Most implemented papers

SRL4ORL: Improving Opinion Role Labeling using Multi-task Learning with Semantic Role Labeling

amarasovic/naacl-mpqa-srl4orl NAACL 2018

For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question "Who expressed what kind of sentiment towards what?".

Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling

zhangmeishan/SRL4ORL NAACL 2019

Opinion role labeling (ORL) is an important task for fine-grained opinion mining, which identifies important opinion arguments such as holder and target for a given opinion trigger.

Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role Labeling

chocowu/syptrtrans-orl 5 Oct 2021

Unified opinion role labeling (ORL) aims to detect all possible opinion structures of 'opinion-holder-target' in one shot, given a text.