Norms of Valence and Arousal for 2,076 Chinese 4-Character Words

  • Pingping LiuEmail author
  • Minglei Li
  • Qin LuEmail author
  • Buxin Han
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 781)


This study describes an annotated dataset through psycho-linguistic annotations in controlled environment on valence and arousal for a large lexicon of 2,076 Chinese 4-character words. The purpose for the annotation is to provide affect-linked knowledge to text which can be used in affective computing using NLP techniques. Analysis to the annotated data indicates that valence and arousal fit the classical U-shaped distribution. Most importantly, the annotated results indicate that the same 2-character word that appears in different 4-character words can indeed show distinct affective meanings which implies that the affective meaning of 4-character words may not be compositional to its component words. The study on this annotated list of 4-character words not only has significance at the intersection of cognitive neuroscience and social psychology, but also has great value as a resource for affective analysis in NLP applications.


Valence Arousal Chinese words Emotion Affective analysis 



This project is supported partially by the CAS Key Laboratory of Mental Health (No. KLMH2014ZG14), the Hong Kong Scholars Program (No. XJ2015050), the National Natural Science Foundation of China (No. 31600887), RGC Funding (Pol- yU152006/16E), and HK Polytechnic University (PolyU RTVU and CERG PolyU 15211/14E).


  1. 1.
    Barrett, L.F., Mesquita, B., Ochsner, K.N., Gross, J.J.: The experience of emotion. Ann. Rev. Psychol. 58, 373–403 (2007)CrossRefGoogle Scholar
  2. 2.
    Schauenburg, G., Ambrasat, J., Schröder, T., vonScheve, C., Conrad, M.: Emotional connotations of words related to authority and community. Behav. Res. Meth. 47(3), 720–735 (2015)CrossRefGoogle Scholar
  3. 3.
    Russell, J.A.: Core affect and the psychological construction of emotion. Psychol. Rev. 110(1), 145–172 (2003)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Ambrasat, J., von Scheve, C., Conrad, M., Schauenburg, G., Schröder, T.: Consensus and stratification in the affective meaning of human sociality. Proc. Nat. Acad. Sci. 111(22), 8001–8006 (2014)CrossRefGoogle Scholar
  5. 5.
    Scott, G.G., O’Donnell, P.J., Sereno, S.C.: Emotion words and categories: evidence from lexical decision. Cognit. Process 15, 209–215 (2014)CrossRefGoogle Scholar
  6. 6.
    Stone, P.J., Dunphy, D.C., Smith, M.S., Ogilvie, D.M.: The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Cambridge, MA (1966)Google Scholar
  7. 7.
    Mehrabian, A.: Framework for a comprehensive description and measurement of emotional states. Genet. Soc. Gen. Psychol. Monogr. 121, 339–361 (1995)Google Scholar
  8. 8.
    Ohana, B., and Tierney, B.: Sentiment classification of reviews using SentiWordNet. In: 9th IT Conference, Dublin Institute of Technology, Dublin, Ireland, pp. 1–9, 22–23 October 2009Google Scholar
  9. 9.
    Baroni, M., Zamparelli, R.: Nouns are vectors, adjectives are matrices: representing adjective-noun constructions in semantic space. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1183–1193 (2010)Google Scholar
  10. 10.
    Mitchell, J., Lapata, M.: Composition in distributional models of semantics. Cognit. Sci. 34(8), 1388–1429 (2010)CrossRefGoogle Scholar
  11. 11.
    Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1631–1642 (2013)Google Scholar
  12. 12.
    Bradley, M.M., Lang, P.J.: Affective norms for English words (ANEW): Instruction manual and affective ratings, Technical report C-1, the center for research in psychophysiology. University of Florida, Gainesville (1999)Google Scholar
  13. 13.
    Warriner, A.B., Kuperman, V., Brysbaert, M.: Norms of valence, arousal, and dominance for 13,915 English lemmas. Behav. Res. Meth. 45(4), 1191–1207 (2013)CrossRefGoogle Scholar
  14. 14.
    Schmidtke, D.S., Schröder, T., Jacobs, A.M., Conrad, M.: ANGST: Affective norms for German sentiment terms, derived from the affective norms for English words. Behav. Res. Meth. 46(4), 1108–1118 (2014)CrossRefGoogle Scholar
  15. 15.
    Monnier, C., Syssau, A.: Affective norms for 720 French words rated by children and adolescents (FANchild). Behav. Res. Meth. 49, 1882–1893 (2017)CrossRefGoogle Scholar
  16. 16.
    Stadthagen-Gonzalez, H., Imbault, C., Sánchez, M.A.P., Brysbaert, M.: Norms of valence and arousal for 14,031 Spanish words. Behav. Res. Meth. 49, 111–123 (2017)CrossRefGoogle Scholar
  17. 17.
    Yao, Z., Wu, J., Zhang, Y., Wang, Z.: Norms of valence, arousal, concreteness, familiarity, image ability, and context availability for 1,100 Chinese words. Behav. Res. Meth. 49, 1374–1385 (2017)CrossRefGoogle Scholar
  18. 18.
    Yu, L.-C., Lee, L.-H., Hao, S., Wang, J., He, Y., Hu, J., Lai, K.R., Zhang, X.: Building Chinese affective resources in valence-arousal dimensions. In: Proceedings of NAACL-HLT, pp. 540–545 (2016)Google Scholar
  19. 19.
    Wang, Y.N., Zhou, L.M., Luo, Y.J.: The pilot establishment and evaluation of Chinese affective word system. Chin. Mental Health J. 22, 39–43 (2008)Google Scholar
  20. 20.
    Chinese Lexicon. Produced by State Key Laboratory of Intelligent Technology and Systems. Tsinghua University and Institute of Automation, Chinese Academy of Sciences. Retrieved from Chinese linguistic Data Consortium Beijing, China (2003)Google Scholar
  21. 21.
    Lexicon of common words in contemporary Chinese (protocol), Produced by Lexicon of common words in contemporary Chinese research team, The Commercial Press, Beijing, China (2008)Google Scholar
  22. 22.
    Mehrabian, A.: Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament. Curr. Psychol. 14(4), 261–292 (1996)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Heise, D.R.: Semantic differential profiles for 1,000 most frequent English words. Psychol. Monogr. Gen. Appl. 79(8), 1–31 (1965)CrossRefGoogle Scholar
  24. 24.
    Hutto, C.J., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media, pp. 216–225 (2014)Google Scholar
  25. 25.
    Heise, D.R.: Affect control theory: concepts and model. J. Math. Sociol. 13(1–2), 1–33 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Li, M., Lu, Q., Long, Y.: Representation learning of multiword expressions with compositionality constraint. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds.) KSEM 2017. LNCS (LNAI), vol. 10412, pp. 507–519. Springer, Cham (2017). CrossRefGoogle Scholar
  27. 27.
    Frege, G.: The Foundations of Arithmetic: A Logico-Mathematical Enquiry into the Concept of Number. Northwestern University Press, Evanston (1980)Google Scholar
  28. 28.
    Zhao, Y., Liu, Z., Sun, M.: Phrase type sensitive tensor indexing model for semantic composition. In: AAAI, pp. 2195–2202 (2015)Google Scholar
  29. 29.
    Calvo, R.A., Mac Kim, S.: Emotions in text: dimensional and categorical models. Comput. Intell. 29(3), 527–543 (2013)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Moors, A., De Houwer, J., Hermans, D., Wanmaker, S., Van Schie, K., Van Harmelen, A.-L., De Schryver, M., De Winne, J., Brysbaert, M.: Norms of valence, arousal, dominance, and age of acquisition for 4,300 Dutchwords. Behav. Res. Meth. 45(1), 169–177 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of ComputingThe Hong Kong Polytechnic UniversityHong KongChina
  2. 2.CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
  3. 3.Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina

Personalised recommendations