Fine-Grained Opinion Analysis
4 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 )
MPQA Opinion Corpus
Most implemented papers
SRL4ORL: Improving Opinion Role Labeling using Multi-task Learning with Semantic Role Labeling
amarasovic/naacl-mpqa-srl4orl
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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
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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
Unified opinion role labeling (ORL) aims to detect all possible opinion structures of 'opinion-holder-target' in one shot, given a text.
Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private States
In this paper, we present a comprehensive study of the entire MPQA 2. 0 dataset.
