Work

Evaluating Distant Supervision for Subjectivity and Sentiment Analysis on Arabic Twitter Feeds

Year: 2014

Type: conference-paper

Abstract: Supervised machine learning methods for automatic subjectivity and sentiment analysis (SSA) are problematic when applied to social media, such as Twitter, since they do not generalise well to unseen topics.A possible remedy of this problem is to apply distant supervision (DS) approaches, which learn from large amounts of automatically annotated data.This research empirically evaluates the performance of DS approaches for SSA on Arabic Twitter feeds.Results for emoticon-and lexiconbased DS show a significant performance gain over a fully supervised baseline, especially for detecting subjectivity, where we achieve 95.19% accuracy, which is a 48.47% absolute improvement over previous fully supervised results.

Source: Arabic Natural Language Processing Workshop (WANLP)

DOI: 10.3115/v1/w14-3624

Authors: Eshrag Refaee, Verena Rieser

Institution: Heriot-Watt University

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W2148263003

FWCI: 1.57

Cites: 24

Cited by: 23

Related to: 10