Skip to main content
SpringerLink
Log in

The good, the bad, and the ambivalent: Extrapolating affective values for 38,000+ Chinese words via a computational model

  • Original Manuscript
  • Published:
Behavior Research Methods Aims and scope Submit manuscript

Abstract

Word affective ratings are important tools in psycholinguistic research, natural language processing, and many other fields. However, even for well-studied languages, such norms are usually limited in scale. To extrapolate affective (i.e., valence and arousal) values for words in the SUBTLEX-CH database (Cai & Brysbaert, 2010, PLoS ONE, 5(6):e10729), we implemented a computational neural network which captured how words’ vector-based semantic representations corresponded to the probability densities of their valence and arousal. Based on these probability density functions, we predicted not only a word’s affective values, but also their respective degrees of variability that could characterize individual differences in human affective ratings. The resulting estimates of affective values largely converged with human ratings for both valence and arousal, and the estimated degrees of variability also captured important features of the variability in human ratings. We released the extrapolated affective values, together with their corresponding degrees of variability, for over 38,000 Chinese words in the Open Science Framework (https://osf.io/s9zmd/). We also discussed how the view of embodied cognition could be illuminated by this computational model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability

The database generated in the present study is available at https://osf.io/s9zmd/.

Notes

  1. We utilized MacBERT-Large as it is the top-performing language model in the Chinese BERT series (Cui et al., 2020). The pretrained model can be obtained from https://github.com/ymcui/MacBERT or accessed through the Hugging Face Python library.

  2. The large corpus consisted of text in both formal and colloquial language. The most substantial portion of text came from the Baidu Baike corpus. In addition, text in the 2019 dump of Wikipedia, News corpus, Weibo, as well as the two widely used corpora, The Lancaster Corpus of Mandarin Chinese (McEnery & Xiao, 2004) and the one issued by the National Language Commission, was also included.

  3. Because Chinese BERT is character-based, the token embedding was generated by averaging those of the character components.

  4. As words have different variabilities along the z.valence.mean and z.arousal.mean scale, this stratification process could not guarantee that a given word was assigned to the same set to train and validate the two separate models for valence and arousal.

  5. The nearest neighbors were identified on the basis of the cosine similarity between BERT type embeddings. To place the models on an even ground for comparison, the baseline model was evaluated using the same set of words as MDNs across the five folds, where the predicted values (i.e., z.valence.mean and z.arousal.mean) of a word in the validation set corresponded to the average value of its k-nearest neighbors in the training set. Given that the predictive performance depends greatly on the number of nearest neighbors, we tested the baseline model by setting the parameter k to 5, 10, 20, 30, 50, 100, 200, and 300. Through five-fold cross-validation, the optimal setting was obtained at k = 20, where the predicted values showed a correlation of 0.870 and 0.757 for z.valence.mean and z.arousal.mean, respectively. These results were reported in Tables 3 and 4 to compare with the predictions based on MDNs.

  6. For a target word in the validation set, we looked for its k-nearest neighbors (k = 20) in the training set based on the cosine similarity between BERT type embeddings. We then took the SDs of their affective values (i.e., z.valence.mean or z.arousal.mean) as the computed variability.

  7. We thank an anonymous reviewer for making this point.

  8. In light of the compositional semantics (Choi & Cardie, 2008; Moilanen & Pulman, 2008), we had also taken a hybrid approach whereby the affective values collected for the constituent characters (Peng et al., 2023) had been incorporated into the current computational model. However, the ablation study showed that the hybrid model performed almost on par with the one based solely on word embeddings, indicating that the contribution of the characters’ affective values was limited, even though they could explain some variance in the words’ affective values and generate decent predictions. For the sake of simplicity, we reported the computational model that was implemented based purely on word embeddings.

  9. Pretrained model was obtained from https://code.google.com/archive/p/word2vec/.

  10. See https://smallworldofwords.org/zh.

References

  • Antoniak, M., & Mimno, D. (2018). Evaluating the stability of embedding-based word similarities. Transactions of the Association for Computational Linguistics, 6, 107–119.

    Article  Google Scholar 

  • Bestgen, Y., & Vincze, N. (2012). Checking and bootstrapping lexical norms by means of word similarity indexes. Behavior Research Methods, 44(4), 998–1006.

    Article  PubMed  Google Scholar 

  • Binder, J. R., Conant, L. L., Humphries, C. J., Fernandino, L., Simons, S. B., Aguilar, M., & Desai, R. H. (2016). Toward a brain-based componential semantic representation. Cognitive Neuropsychology, 33(3–4), 130–174.

    Article  PubMed  Google Scholar 

  • Bishop, C. M. (1994). Mixture density network (Technical Report No. NCRG/94/004). Birmingham, UK: Aston University, Neural Computing Research Group.

  • Bommasani, R., Davis, K., & Cardie, C. (2020). Interpreting pretrained contextualized representations via reductions to static embeddings. In: Paper presented at the Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 4758–4781). Online.

  • Bradley, M. M., & Lang, P. J. (1999). Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings (Technical Report No. C-1). Gainesville, USA: University of Florida, NIMH Center for Research in Psychophysiology.

  • Cai, Q., & Brysbaert, M. (2010). SUBTLEX-CH: Chinese word and character frequencies based on film subtitles. PLoS ONE, 5(6), e10729.

    Article  PubMed  PubMed Central  Google Scholar 

  • Calderon-Delgado, L., Barrera-Valencia, M., Noriega, I., Al-Khalil, K., Trejos-Castillo, E., Mosi, J., Chavez, B., Galvan, M., & O’Boyle, M. W. (2020). Implicit processing of emotional words by children with post-traumatic stress disorder: An fMRI investigation. International Journal of Clinical and Health Psychology, 20(1), 46–53.

    Article  PubMed  Google Scholar 

  • Calvo, R. A., & D’Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), 18–37.

    Article  Google Scholar 

  • Chersoni, E., Santus, E., Huang, C. R., & Lenci, A. (2021). Decoding word embeddings with brain-based semantic features. Computational Linguistics, 47(3), 663–698.

    Article  Google Scholar 

  • Choi, Y., & Cardie, C. (2008). Learning with compositional semantics as structural inference for subsentential sentiment analysis. In: Paper presented at the Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (pp. 793–801). Honolulu, USA.

  • Citron, F. M., Weekes, B. S., & Ferstl, E. C. (2014). Arousal and emotional valence interact in written word recognition. Language, Cognition and Neuroscience, 29(10), 1257–1267.

    Article  Google Scholar 

  • Ćoso, B., Guasch, M., Ferré, P., & Hinojosa, J. A. (2019). Affective and concreteness norms for 3,022 Croatian words. Quarterly Journal of Experimental Psychology, 72(9), 2302–2312.

    Article  Google Scholar 

  • Cui, Y., Che, W., Liu, T., Qin, B., Wang, S., & Hu, G. (2020). Revisiting pre-trained models for Chinese natural language processing. In: Paper presented at the Findings of the Association for Computational Linguistics: Empirical Methods in Natural Language Processing 2020 (pp. 657–668). Online.

  • De Deyne, S., & Storms, G. (2008). Word associations: Network and semantic properties. Behavior Research Methods, 40(1), 213–231.

    Article  PubMed  Google Scholar 

  • De Deyne, S., & Storms, G. (2008). Word associations: Norms for 1,424 Dutch words in a continuous task. Behavior Research Methods, 40(1), 198–205.

    Article  PubMed  Google Scholar 

  • De Deyne, S., Navarro, D. J., & Storms, G. (2013). Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations. Behavior Research Methods, 45(2), 480–498.

    Article  PubMed  Google Scholar 

  • De Deyne, S., Verheyen, S., & Storms, G. (2015). The role of corpus size and syntax in deriving lexico-semantic representations for a wide range of concepts. Quarterly Journal of Experimental Psychology, 68(8), 1643–1664.

    Article  Google Scholar 

  • De Deyne, S., Navarro, D. J., Perfors, A., Brysbaert, M., & Storms, G. (2019). The “Small World of Words” English word association norms for over 12,000 cue words. Behavior Research Methods, 51(3), 987–1006.

    Article  PubMed  Google Scholar 

  • Deese, J. (1966). The structure of associations in language and thought. Johns Hopkins University Press.

    Google Scholar 

  • Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pretraining of deep bidirectional transformers for language understanding. In: Paper presented at the Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171–4186). Minneapolis, USA.

  • Endres, M. J., & Fein, G. (2013). Emotion-word processing difficulties in abstinent alcoholics with and without lifetime externalizing disorders. Alcoholism: Clinical and Experimental Research, 37(5), 831–838.

    Article  PubMed  Google Scholar 

  • Ethayarajh, K. (2019). How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings. In: Paper presented at the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (pp. 55–65). Hong Kong, China.

  • Fazio, R. H. (2001). On the automatic activation of associated evaluations: An overview. Cognition & Emotion, 15(2), 115–141.

    Article  Google Scholar 

  • Fraga, I., Guasch, M., Haro, J., Padrón, I., & Ferré, P. (2018). EmoFinder: The meeting point for Spanish emotional words. Behavior Research Methods, 50(1), 84–93.

    Article  PubMed  Google Scholar 

  • Grandy, T. H., Lindenberger, U., & Schmiedek, F. (2020). Vampires and nurses are rated differently by younger and older adults—Age-comparative norms of imageability and emotionality for about 2500 German nouns. Behavior Research Methods, 52(3), 980–989.

    Article  PubMed  PubMed Central  Google Scholar 

  • Günther, F., Rinaldi, L., & Marelli, M. (2019). Vector-space models of semantic representation from a cognitive perspective: A discussion of common misconceptions. Perspectives on Psychological Science, 14(6), 1006–1033.

    Article  PubMed  Google Scholar 

  • Günther, F., Petilli, M. A., Vergallito, A., & Marelli, M. (2022). Images of the unseen: Extrapolating visual representations for abstract and concrete words in a data-driven computational model. Psychological Research, 86(8), 2512–2532.

    Article  PubMed  Google Scholar 

  • Hinojosa, J. A., Moreno, E. M., & Ferre, P. (2020). Affective neurolinguistics: Towards a framework for reconciling language and emotion. Language, Cognition and Neuroscience, 35(7), 813–839.

    Article  Google Scholar 

  • Hollis, G. (2017). Estimating the average need of semantic knowledge from distributional semantic models. Memory & Cognition, 45(8), 1350–1370.

    Article  Google Scholar 

  • Hollis, G., Westbury, C., & Lefsrud, L. (2017). Extrapolating human judgments from skip-gram vector representations of word meaning. Quarterly Journal of Experimental Psychology, 70(8), 1603–1619.

    Article  Google Scholar 

  • Humphreys, G. F., Hoffman, P., Visser, M., Binney, R. J., & Ralph, M. A. L. (2015). Establishing task- and modality-dependent dissociations between the semantic and default mode networks. Proceedings of the National Academy of Sciences, 112(25), 7857–7862.

    Article  Google Scholar 

  • Imbir, K. K. (2021). Affective Norms for 4900 Polish Words Reload (ANPW_R): Assessments for valence, arousal, dominance, origin, significance, concreteness, imageability and age of acquisition. Frontiers in Psychology, 12(7), 1081–2016.

    Google Scholar 

  • Inohara, K., & Utsumi, A. (2022). JWSAN: Japanese word similarity and association norm. Language Resources and Evaluation, 56(1), 109–137.

    Article  Google Scholar 

  • Islam, M. R., & Zibran, M. F. (2018). SentiStrength-SE: Exploiting domain specificity for improved sentiment analysis in software engineering text. Journal of Systems and Software, 145, 125–146.

    Article  Google Scholar 

  • Kapucu, A., Kılıç, A., Ӧzkılıç, Y., & Sarıbaz, B. (2021). Turkish emotional word norms for arousal, valence, and discrete emotion categories. Psychological Reports, 124(1), 188–209.

    Article  PubMed  Google Scholar 

  • Kousta, S. T., Vinson, D. P., & Vigliocco, G. (2009). Emotion words, regardless of polarity, have a processing advantage over neutral words. Cognition, 112(3), 473–481.

    Article  PubMed  Google Scholar 

  • Kousta, S. T., Vigliocco, G., Vinson, D. P., Andrews, M., & Del Campo, E. (2011). The representation of abstract words: Why emotion matters. Journal of Experimental Psychology: General, 140(1), 14–34.

    Article  PubMed  Google Scholar 

  • Kuperman, V., Estes, Z., Brysbaert, M., & Warriner, A. B. (2014). Emotion and language: Valence and arousal affect word recognition. Journal of Experimental Psychology: General, 143(3), 1065–1081.

    Article  PubMed  Google Scholar 

  • Lahl, O., Gӧritz, A. S., Pietrowsky, R., & Rosenberg, J. (2009). Using the World-Wide Web to obtain large-scale word norms: 190,212 ratings on a set of 2,654 German nouns. Behavior Research Methods, 41(1), 13–19.

    Article  PubMed  Google Scholar 

  • Lambon Ralph, M. A., Jefferies, E., Patterson, K., & Rogers, T. T. (2017). The neural and computational bases of semantic cognition. Nature Reviews Neuroscience, 18(1), 42–55.

    Article  Google Scholar 

  • Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104(2), 211–240.

    Article  Google Scholar 

  • Lenci, A., Lebani, G. E., & Passaro, L. C. (2018). The emotions of abstract words: A distributional semantic analysis. Topics in Cognitive Science, 10(3), 550–572.

    Article  PubMed  Google Scholar 

  • Lenci, A., Sahlgren, M., Jeuniaux, P., Gyllensten, A. C., & Miliani, M. (2022). A comparative evaluation and analysis of three generations of distributional semantic models. Language Resources and Evaluation, 56(4), 1269–1313.

    Article  Google Scholar 

  • Li, S., Zhao, Z., Hu, R., Li, W., Liu, T., & Du, X. (2018). Analogical reasoning on Chinese morphological and semantic relations. In: Paper presented at the Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (pp. 138–143). Melbourne, Australia.

  • Liu, P., Lu, Q., Zhang, Z., Tang, J., & Han, B. (2021). Age-related differences in affective norms for Chinese words (AANC). Frontiers in Psychology, 12, 585666.

  • Mandera, P., Keuleers, E., & Brysbaert, M. (2015). How useful are corpus-based methods for extrapolating psycholinguistic variables? Quarterly Journal of Experimental Psychology, 68(8), 1623–1642.

    Article  Google Scholar 

  • Mandera, P., Keuleers, E., & Brysbaert, M. (2017). Explaining human performance in psycholinguistic tasks with models of semantic similarity based on prediction and counting: A review and empirical validation. Journal of Memory and Language, 92, 57–78.

    Article  Google Scholar 

  • Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. MIT Press.

    Google Scholar 

  • Martin, C. B., Douglas, D., Newsome, R. N., Man, L. L. Y., & Barense, M. D. (2018). Integrative and distinctive coding of visual and conceptual object features in the ventral visual stream. eLife, 7, e31873.

    Article  PubMed  PubMed Central  Google Scholar 

  • Martínez-Huertas, J. A., Jorge-Botana, G., Luzón, J. M., & Olmos, R. (2021). Redundancy, isomorphism, and propagative mechanisms between emotional and amodal representations of words: A computational study. Memory & Cognition, 49(2), 219–234.

    Article  Google Scholar 

  • McEnery, A., & Xiao, Z. (2004). The Lancaster Corpus of Mandarin Chinese: A Corpus for monolingual and contrastive language study. In: Paper presented at the Proceedings of the 4th International Conference on Language Resources and Evaluation, Lisbon, Portugal.

  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. ArXiv:1301.3781.

  • Miller, G. A., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. (1990). WordNet: An on-line lexical database. International Journal of Lexicography, 3, 235–244.

    Article  Google Scholar 

  • Moilanen, K., & Pulman, S. (2008). The good, the bad, and the unknown: Morphosyllabic sentiment tagging of unseen words. In: Paper presented at the Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies (pp. 109–112). Columbus, USA.

  • Monnier, C., & Syssau, A. (2014). Affective norms for French words (FAN). Behavior Research Methods, 46(4), 1128–1137.

    Article  PubMed  Google Scholar 

  • Montefinese, M., Ambrosini, E., Fairfield, B., & Mammarella, N. (2014). The adaptation of the Affective Norms for English Words (ANEW) for Italian. Behavior Research Methods, 46(3), 887–903.

    Article  PubMed  Google Scholar 

  • Moors, A., De Houwer, J., Hermans, D., Wanmaker, S., van Schie, K., van Harmelen, A. L., De Schryver, M., De Winne, J., & Brysbaert, M. (2013). Norms of valence, arousal, dominance, and age of acquisition for 4,300 Dutch words. Behavior Research Methods, 45(1), 169–177.

    Article  PubMed  Google Scholar 

  • Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The measurement of meaning. University of Illinois Press.

    Google Scholar 

  • Patterson, K., Nestor, P. J., & Rogers, T. T. (2007). Where do you know what you know? The representation of semantic knowledge in the human brain. Nature Reviews Neuroscience, 8(12), 976–987.

    Article  PubMed  Google Scholar 

  • Peng, C., Xu, X., & Bao, Z. (2023). Sentiment annotations for 3,827 simplified Chinese characters. Behavioral Research Methods.

  • Petilli, M. A., Günther, F., Vergallito, A., Ciapparelli, M., & Marelli, M. (2021). Data-driven computational models reveal perceptual simulation in word processing. Journal of Memory and Language, 117, 104194.

    Article  Google Scholar 

  • Plaut, D. C., & Booth, J. R. (2000). Individual and developmental differences in semantic priming: Empirical and computational support for a single-mechanism account of lexical processing. Psychological Review, 107(4), 786–823.

    Article  PubMed  Google Scholar 

  • Pobric, G., Jefferies, E., & Lambon Ralph, M. A. (2007). Anterior temporal lobes mediate semantic representation: Mimicking semantic dementia by using rTMS in normal participants. Proceedings of the National Academy of Sciences, 104(50), 20137–20141.

    Article  Google Scholar 

  • Pollock, L. (2018). Statistical and methodological problems with concreteness and other semantic variables: A list memory experiment case study. Behavior Research Methods, 50(3), 1198–1216.

    Article  PubMed  Google Scholar 

  • Qiu, Y., Li, H., Li, S., Jiang, Y., Hu, R., & Yang, L. (2018). Revisiting correlations between intrinsic and extrinsic evaluations of word embeddings. In M. Sun, T. Liu, X. Wang, Z. Liu, & Y. Liu (Eds.), Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (pp. 209–221). Springer.

    Chapter  Google Scholar 

  • Reagan, A. J., Danforth, C. M., Tivnan, B., Williams, J. R., & Dodds, P. S. (2017). Sentiment analysis methods for understanding large-scale texts: A case for using continuum-scored words and word shift graphs. EPJ Data Science, 6, 28.

  • Recchia, G., & Louwerse, M. M. (2015). Reproducing affective norms with lexical co-occurrence statistics: Predicting valence, arousal, and dominance. Quarterly Journal of Experimental Psychology, 68(8), 1584–1598.

    Article  Google Scholar 

  • Riegel, M., Wierzba, M., Wypych, M., Żurawski, Ł, Jednoróg, K., Grabowska, A., & Marchewka, A. (2015). Nencki Affective Word List (NAWL): the cultural adaptation of the Berlin Affective Word List-Reloaded (BAWL-R) for Polish. Behavior Research Methods, 47(4), 1222–1236.

    Article  PubMed  PubMed Central  Google Scholar 

  • Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178.

    Article  Google Scholar 

  • Sommerauer, P., & Fokkens, A. (2018). Firearms and tigers are dangerous, kitchen knives and zebras are not: Testing whether word embeddings can tell. In: Paper presented at the Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (pp. 276–286). Brussels, Belgium.

  • Stadthagen-Gonzalez, H., Imbault, C., Sánchez, M. A. P., & Brysbaert, M. (2017). Norms of valence and arousal for 14,031 Spanish words. Behavior Research Methods, 49(1), 111–123.

    Article  PubMed  Google Scholar 

  • Steiger, J. H. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87(2), 245–251.

    Article  Google Scholar 

  • Szalay, L. B., & Deese, J. (1978). Subjective meaning and culture: An assessment through word associations. Lawrence Erlbaum.

    Google Scholar 

  • Tsang, Y. K., Huang, J., Lui, M., Xue, M., Chan, Y. W. F., Wang, S., & Chen, H. C. (2018). MELD-SCH: A megastudy of lexical decision in simplified Chinese. Behavior Research Methods, 50(5), 1763–1777.

    Article  PubMed  Google Scholar 

  • Turney, P. D., & Littman, M. L. (2003). Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems, 21(4), 315–346.

    Article  Google Scholar 

  • Utsumi, A. (2020). Exploring what is encoded in distributional word vectors: A neurobiologically motivated analysis. Cognitive Science, 44(6), e12844.

  • Van Rensbergen, B., Storms, G., & De Deyne, S. (2015). Examining assortativity in the mental lexicon: Evidence from word associations. Psychonomic Bulletin & Review, 22(6), 1717–1724.

    Article  Google Scholar 

  • Van Rensbergen, B., De Deyne, S., & Storms, G. (2016). Estimating affective word covariates using word association data. Behavior Research Methods, 48(4), 1644–1652.

    Article  PubMed  Google Scholar 

  • Verona, E., Sprague, J., & Sadeh, N. (2012). Inhibitory control and negative emotional processing in psychopathy and antisocial personality disorder. Journal of Abnormal Psychology, 121(2), 498–510.

    Article  PubMed  Google Scholar 

  • Võ, M. L. H., Conrad, M., Kuchinke, L., Urton, K., Hofmann, M. J., & Jacobs, A. M. (2009). The Berlin Affective Word List Reloaded (BAWL-R). Behavior Research Methods, 41(2), 534–538.

    Article  PubMed  Google Scholar 

  • Vulić, I., Ponti, E. M., Litschko, R., Glavaš, G., & Korhonen, A. (2020). Probing pretrained language models for lexical semantics. In: Paper presented at the Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (pp. 7222–7240). Online.

  • Wang, Y., Zhou, L., & Luo, Y. (2008). The pilot establishment and evaluation of Chinese affective words system. Chinese Mental Health Journal, 22(8), 608–612.

    Google Scholar 

  • Wang, X., Wu, W., Ling, Z., Xu, Y., Fang, Y., Wang, X., Binder, J. R., Men, W., Gao, J., & Bi, Y. (2018). Organizational principles of abstract words in the human brain. Cerebral Cortex, 28, 4305–4318.

    Article  PubMed  Google Scholar 

  • Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013). Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods, 45(4), 1191–1207.

    Article  PubMed  Google Scholar 

  • Wrobel, M. R. (2020). The impact of lexicon adaptation on the emotion mining from software engineering artifacts. IEEE Access, 8, 48742–48751.

    Article  Google Scholar 

  • Xu, X., & Li, J. (2020). Concreteness/abstractness ratings for two-character Chinese words in MELD-SCH. Plos One, 15(6), e0232133.

    Article  PubMed  PubMed Central  Google Scholar 

  • Xu, X., Li, J., & Guo, S. (2021). Age of acquisition ratings for 19,716 simplified Chinese words. Behavior Research Methods, 53, 558–573.

    Article  PubMed  Google Scholar 

  • Xu, X., Li, J., & Chen, H. (2022). Valence and arousal ratings for 11,310 simplified Chinese words. Behavior Research Methods, 54, 26–41.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Dr. Xurong Xie and Dr. Rongfeng Su for discussing the implementation of the computational model. The computations were performed using research computing facilities offered by Information Technology Services, the University of Hong Kong. This study is supported by research grants awarded to the corresponding author by Shanghai Jiao Tong University (WF220414005). We would also like to express our gratitude to two anonymous reviewers for their helpful comments and suggestions.

Author information

Authors and Affiliations

  1. School of Foreign Languages, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai, 200240, China

    Tianqi Wang & Xu Xu

  2. Speech Science Laboratory, The University of Hong Kong, Hong Kong, China

    Tianqi Wang

  3. Academic Unit of Human Communication, Development, and Information Sciences, The University of Hong Kong, Hong Kong, China

    Tianqi Wang

Authors
  1. Tianqi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xu Xu.

Ethics declarations

Conflict of Interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Practices Statement

The extrapolated database can be accessed from the Open Science Framework repository. The study was not preregistered.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 909 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, T., Xu, X. The good, the bad, and the ambivalent: Extrapolating affective values for 38,000+ Chinese words via a computational model. Behav Res (2023). https://doi.org/10.3758/s13428-023-02274-3

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.3758/s13428-023-02274-3

Keywords

Search

Navigation