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 )
MPQA Opinion Corpus
Latest papers with no code
Fine-Grained Opinion Summarization with Minimal Supervision
Opinion summarization aims to profile a target by extracting opinions from multiple documents.
Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks
The experimental results show that syntactic information is highly valuable for ORL, and our final MTL model effectively boosts the F1 score by 9. 29 over the syntax-agnostic baseline.
The 2018 Shared Task on Extrinsic Parser Evaluation: On the Downstream Utility of English Universal Dependency Parsers
We summarize empirical results and tentative conclusions from the Second Extrinsic Parser Evaluation Initiative (EPE 2018).
Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction
Fine-grained opinion analysis aims to extract aspect and opinion terms from each sentence for opinion summarization.
Toward Stance Classification Based on Claim Microstructures
Claims are the building blocks of arguments and the reasons underpinning opinions, thus analyzing claims is important for both argumentation mining and opinion mining.
Joint Modeling of Opinion Expression Extraction and Attribute Classification
In this paper, we study the problems of opinion expression extraction and expression-level polarity and intensity classification.
