Earticle

초록

영어

This paper addresses the problem of classifying news headlines into sentiment categories. Using a supervised approach, we train a classifier for classifying each news headline as positive, negative, or neutral. A news headline is considered positive if it is associated with good things, negative if it is associated with bad things, and neutral in the remaining cases. The experiments show an accuracy that ranges from 59.00% to 63.50% when syntactic features (argument1-verb-argument2 relations) are combined with other features. The accuracy ranges from 57.50% to 62.5% when these relations are not used.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Text and Sentiment Classification
  2.2. Sentiment Classification of News Articles
 3. Classifying News Headlines
  3.1. Applied Approaches - Overview
  3.2. Extracting Argument1-verb-argument2 Relations
 4. Experiments and Results
  4.1. Datasets
  4.2. Sentiment Classification - Evaluation
  4.3. Relation Extraction - Evaluation
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• António Paulo Santos [ GECAD, Institute of Engineering - Polytechnic of Porto, Portugal ]
• Carlos Ramos [ GECAD, Institute of Engineering - Polytechnic of Porto, Portugal ]
• Nuno C. Marques [ NOVA Laboratory for Computer Science and informatics, DI-FCT, Universidade Nova de Lisboa, Monte da Caparica, Portugal ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605