Browse > Natural Language Processing > Sentiment Analysis > Fine-Grained Opinion Analysis

Fine-Grained Opinion Analysis

2 papers with code · Natural Language Processing
Subtask of Sentiment Analysis

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.

State-of-the-art leaderboards

Greatest papers with code

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

NAACL 2018 amarasovic/naacl-mpqa-srl4orl

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?".

 SOTA for Fine-Grained Opinion Analysis on MPQA (using extra training data)

FINE-GRAINED OPINION ANALYSIS MULTI-TASK LEARNING

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

NAACL 2019 zhangmeishan/SRL4ORL

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.

 SOTA for Fine-Grained Opinion Analysis on MPQA (using extra training data)

FINE-GRAINED OPINION ANALYSIS OPINION MINING SEMANTIC ROLE LABELING