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Fine-Grained Opinion Analysis Edit

2 papers with code · Natural Language Processing

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 )

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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.

SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling

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?{''}.

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.