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Fine-Grained Opinion Analysis

2 papers with code ยท 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.

( Image credit: SRL4ORL )

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The 2018 Shared Task on Extrinsic Parser Evaluation: On the Downstream Utility of English Universal Dependency Parsers

CONLL 2018

We summarize empirical results and tentative conclusions from the Second Extrinsic Parser Evaluation Initiative (EPE 2018).

FINE-GRAINED OPINION ANALYSIS

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

NAACL 2018

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?{''}.

FINE-GRAINED OPINION ANALYSIS MULTI-TASK LEARNING SEMANTIC ROLE LABELING

Toward Stance Classification Based on Claim Microstructures

WS 2017

Claims are the building blocks of arguments and the reasons underpinning opinions, thus analyzing claims is important for both argumentation mining and opinion mining.

ARGUMENT MINING FINE-GRAINED OPINION ANALYSIS OPINION MINING

Joint Modeling of Opinion Expression Extraction and Attribute Classification

TACL 2014

In this paper, we study the problems of opinion expression extraction and expression-level polarity and intensity classification.

FINE-GRAINED OPINION ANALYSIS OPINION MINING QUESTION ANSWERING