Work

OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random Fields

Year: 2017

Type: conference-paper

Abstract: We describe a supervised system that uses optimized Conditional Random Fields and lexical features to predict the sentiment of a tweet.The system was submitted to the English version of all subtasks in SemEval-2017 Task 4.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/s17-2111

Authors: Chukwuyem Onyibe, Nizar Habash

Institution: New York University Abu Dhabi

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W2751015782

FWCI: 0.873

Cites: 14

Cited by: 10

Related to: 10