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Authors: nan

Publication: TIPSTER (2015)

DOI: nan

This paper provides a detailed description of the approach of our system for the Aspect- Based Sentiment Analysis task of SemEval- 2015. The task is to identify the Aspect Cate- …...

22620

INF-UFRGS at SemEval-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and Headlines

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThis paper describes a supervised solution for detecting the polarity scores of tweets or headline news in the financial domain, submitted to the SemEval 2017 Fine-Grained Sentiment Analysis on Financial …...

25204

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

Sentiment-based Candidate Selection for NMT

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractThe explosion of user-generated content (UGC)—e.g. social media posts and comments and and reviews—has motivated the development of NLP applications tailored to these types of informal texts. Prevalent among these …...

18336

Investigating the Relationship Between Romanian Financial News and Closing Prices from the Bucharest Stock Exchange

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractA new data set is gathered from a Romanian financial news website for the duration of four years. It is further refined to extract only information related to one company …...

14688

YNU-HPCC at SemEval-2022 Task 4: Finetuning Pretrained Language Models for Patronizing and Condescending Language Detection

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractThis paper describes a system built in the SemEval-2022 competition. As participants in Task 4: Patronizing and Condescending Language Detection, we implemented the text sentiment classification system for two subtasks …...

25112