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task ID: finegrainedsentimentanalysis

Related Papers

Grammatical structures for word-level sentiment detection

Authors: nan

Publication: TIPSTER (2012)

DOI: nan

Existing work in fine-grained sentiment anal- ysis focuses on sentences and phrases but ig- nores the contribution of individual words and their grammatical connections. This is because of a lack …...

23821

DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractWe present the system developed by the team DUTH for the participation in Semeval-2017 task 5 - Fine-Grained Sentiment Analysis on Financial Microblogs and News, in subtasks A and B. …...

25193

ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThis paper describes our systems submitted to the Fine-Grained Sentiment Analysis on Financial Microblogs and News task (i.e., Task 5) in SemEval-2017. This task includes two subtasks in microblogs and …...

25200

IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThis paper reports team IITPB’s participation in the SemEval 2017 Task 5 on ‘Fine-grained sentiment analysis on financial microblogs and news’. We developed 2 systems for the two tracks. One …...

25189

SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThis paper discusses the “Fine-Grained Sentiment Analysis on Financial Microblogs and News” task as part of SemEval-2017, specifically under the “Detecting sentiment, humour, and truth” theme. This task contains two …...

25282

SSN_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThe system developed by the SSN_MLRG1 team for Semeval-2017 task 5 on fine-grained sentiment analysis uses Multiple Kernel Gaussian Process for identifying the optimistic and pessimistic sentiments associated with companies …...

25202

UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThis paper discusses the approach taken by the UWaterloo team to arrive at a solution for the Fine-Grained Sentiment Analysis problem posed by Task 5 of SemEval 2017. The paper …...

25191

Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractWe consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL). Our neural model is trained on document sentiment labels, and learns to predict the …...

27252

Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractA fundamental task of fine-grained sentiment analysis is aspect and opinion terms extraction. Supervised-learning approaches have shown good results for this task; however, they fail to scale across domains where …...

4199

If you’ve got it, flaunt it: Making the most of fine-grained sentiment annotations

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractFine-grained sentiment analysis attempts to extract sentiment holders, targets and polar expressions and resolve the relationship between them, but progress has been hampered by the difficulty of annotation. Targeted sentiment …...

42909