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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 1–10 of 19 papers

TitleStatusHype
Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role LabelingCode0
Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role LabelingCode0
Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private StatesCode0
SRL4ORL: Improving Opinion Role Labeling using Multi-task Learning with Semantic Role LabelingCode0
Joint Inference for Fine-grained Opinion Extraction—0
Joint Modeling of Opinion Expression Extraction and Attribute Classification—0
Mining Fine-grained Opinion Expressions with Shallow Parsing—0
On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics—0
Opinion Mining with Deep Recurrent Neural Networks—0
Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction—0
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