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Fine-Grained Opinion 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.

( Image credit: SRL4ORL )

Papers

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Showing 1–19 of 19 papers

TitleStatusHype
Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private StatesCode0
Fine-Grained Opinion Summarization with Minimal Supervision—0
Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role LabelingCode0
Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks—0
Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role LabelingCode0
The 2018 Shared Task on Extrinsic Parser Evaluation: On the Downstream Utility of English Universal Dependency Parsers—0
Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction—0
SRL4ORL: Improving Opinion Role Labeling using Multi-task Learning with Semantic Role LabelingCode0
Toward Stance Classification Based on Claim Microstructures—0
Investigating LSTMs for Joint Extraction of Opinion Entities and Relations—0
Annotating Targets of Opinions in Arabic using Crowdsourcing—0
On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics—0
Opinion Mining with Deep Recurrent Neural Networks—0
Joint Modeling of Opinion Expression Extraction and Attribute Classification—0
Why Words Alone Are Not Enough: Error Analysis of Lexicon-based Polarity Classifier for Czech—0
Mining Fine-grained Opinion Expressions with Shallow Parsing—0
Joint Inference for Fine-grained Opinion Extraction—0
Relational Features in Fine-Grained Opinion Analysis—0
Extracting Opinion Expressions with semi-Markov Conditional Random Fields—0
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