Reader emotion prediction using concept and concept sequence features in news headlines

Yuanlin Yao, Ruifeng Xu, Qin Lu, Bin Liu, Jun Xu, Chengtian Zou, Li Yuan, Shuwei Wang, Lin Yao, Zhenyu He

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)


This paper presents a method to predicate news reader emotions. News headlines supply core information of articles, thus they can serve as key information for reader emotion predication. However, headlines are always short which leads to obvious data sparseness if only lexical forms are used. To address this problem, words in their lexical forms in a headline are transferred to their concepts and concept sequence features of words in headlines based on a semantic knowledge base, namely HowNet for Chinese. These features are expected to represent the major elements which can evoke reader's emotional reactions. These transferred concepts are used with lexical features in headlines for predicating the reader's emotion. Evaluations on dataset of Sina Social News with user emotion votes show that the proposed approach which do not use any news content, achieves a comparable performance to Bag-Of-Word model using both the headlines and the news contents, making our method more efficient in reader emotion prediction.
Original languageEnglish
Title of host publicationComputational Linguistics and Intelligent Text Processing - 15th International Conference, CICLing 2014, Proceedings
PublisherSpringer Verlag
Number of pages12
EditionPART 2
ISBN (Print)9783642549021
Publication statusPublished - 1 Jan 2014
Event15th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2014 - Kathmandu, Nepal
Duration: 6 Apr 201412 Apr 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8404 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2014


  • Concept Feature
  • Concept Sequence Feature
  • Emotion Prediction
  • HowNet

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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