Norms of valence, arousal, and dominance for 13,915 English lemmas
Open accessAbstract
Information about the affective meanings of words is used by researchers working on emotions and moods, word recognition and memory, and text-based sentiment analysis. Three components of emotions are traditionally distinguished: valence (the pleasantness of a stimulus), arousal (the intensity of emotion provoked by a stimulus), and dominance (the degree of control exerted by a stimulus). Thus far, nearly all research has been based on the ANEW norms collected by Bradley and Lang (1999) for 1,034 words. We extended that database to nearly 14,000 English lemmas, providing researchers with a much richer source of information, including gender, age, and educational differences in emotion norms. As an example of the new possibilities, we included stimuli from nearly all of the category norms (e.g., types of diseases, occupations, and taboo words) collected by Van Overschelde, Rawson, and Dunlosky (Journal of Memory and Language 50:289-335, 2004), making it possible to include affect in studies of semantic memory.
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Warriner et al. (2013) present VAD ratings for 13,914 common English words, providing an empirically validated way of assessing the overall emotional impact of a word.
“Warriner et al. (2013) present VAD ratings for 13,914 common English words, thereby providing an empirically validated way of assessing the overall emotional impact of a word, making mean VAD easy to calculate.”
From Five studies evaluating the impact on mental health and mood of recalling, reading, and discussing fiction (2022) · cites this paper as Warriner et al. (2013)
The Stimuli section reports ratings on three emotional dimensions for common English words, and the Reliability section evaluates the ratings against earlier norms. However, the Stimuli section gives the total as 13,915 lemmas, not 13,914 words. Page numbers are unavailable in our copy. (LLM, conf=0.90)
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Cites
Cohen, J. (1992) · Psychological Bulletin · doi:10.1037//0033-2909.112.1.155 · Cited by 8,771 · see citing works
Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957) · Cited by 8,695 · see citing works
Landauer, T. K., & Dumais, S. T. (1997) · Psychological Review · doi:10.1037/0033-295x.104.2.211 · Cited by 6,282 · see citing works
Sentiment Analysis and Opinion Mining
Zhang, L., & Liu, B. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_907 · Cited by 3,299 · see citing works
Brysbaert, M., & New, B. (2009) · Behavior Research Methods · doi:10.3758/brm.41.4.977 · Cited by 2,821 · see citing works
Self-citationBalota, D. A., Yap, M. J.-M., Hutchison, K. A., Cortese, M. J., Brett Kessler, A., Loftis, B., Neely, J. H., Nelson, D. L., Simpson, G. B., & Rebecca Treiman, A. (2007) · Behavior Research Methods · doi:10.3758/bf03193014 · Cited by 2,758 · see citing works
Sentiment Analysis and Opinion Mining
Liu, B. (2012) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-02145-9 · Cited by 1,872 · see citing works
Age-of-acquisition ratings for 30,000 English words
Kuperman, V., Stadthagen-González, H., & Brysbaert, M. (2012) · Behavior Research Methods · doi:10.3758/s13428-012-0210-4 · Cited by 1,222 · see citing works
Self-citationA meta-analytic review of research on gender differences in sexuality, 1993–2007.
Petersen, J. L., & Hyde, J. S. (2010) · Psychological Bulletin · doi:10.1037/a0017504 · Cited by 931 · see citing works
The representation of abstract words: Why emotion matters.
Kousta, S. T., Vigliocco, G., Vinson, D., Andrews, M. W., & Campo, E. D. (2010) · Journal of Experimental Psychology General · doi:10.1037/a0021446 · Cited by 897 · see citing works
Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon
Mohammad, S. M., & Turney, P. D. (2010) · NPARC · Cited by 882 · see citing works
Age-of-acquisition, imagery, concreteness, familiarity, and ambiguity measures for 1,944 words
Gilhooly, K. J., & Logie, R. H. (1980) · Behavior Research Methods · doi:10.3758/bf03201693 · Cited by 877 · see citing works
Gender differences in language use: An analysis of 14,000 text samples
Newman, M. L., Groom, C. J., Handelman, L. D., & Pennebaker, J. W. (2008) · Discourse Processes · doi:10.1080/01638530802073712 · Cited by 788 · see citing works
Category norms: An updated and expanded version of the Battig and Montague (1969) norms
Overschelde, J. P. V., Rawson, K. A., & Dunlosky, J. T. (2003) · Journal of Memory and Language · doi:10.1016/j.jml.2003.10.003 · Cited by 621 · see citing works
Emotion words, regardless of polarity, have a processing advantage over neutral words
Kousta, S. T., Vinson, D., & Vigliocco, G. (2009) · Cognition · doi:10.1016/j.cognition.2009.06.007 · Cited by 577 · see citing works
SUBTLEX-NL: A new measure for Dutch word frequency based on film subtitles
Keuleers, E., Brysbaert, M., & New, B. (2010) · Behavior Research Methods · doi:10.3758/brm.42.3.643 · Cited by 574 · see citing works
Self-citationKeuleers, E., Lacey, P., Rastle, K., & Brysbaert, M. (2011) · Behavior Research Methods · doi:10.3758/s13428-011-0118-4 · Cited by 391 · see citing works
Self-citationThe Spanish adaptation of ANEW (Affective Norms for English Words)
Redondo, J., Fraga, I., Padrón, I., & Comesaña, M. (2007) · Behavior Research Methods · doi:10.3758/bf03193031 · Cited by 351 · see citing works
Imageability ratings for 3,000 monosyllabic words
Cortese, M. J., & Fugett, A. D. (2004) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03195585 · Cited by 335 · see citing works
Altarriba, J., Bauer, L., & Benvenuto, C. (1999) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03200738 · Cited by 330 · see citing works
The Bristol norms for age of acquisition, imageability, and familiarity
Stadthagen-Gonzalez, H., & Davis, C. J. (2006) · Behavior Research Methods · doi:10.3758/bf03193891 · Cited by 315 · see citing works
Morphological influences on the recognition of monosyllabic monomorphemic words
Baayen, R. H., Feldman, L. B., & Schreuder, R. (2006) · Journal of Memory and Language · doi:10.1016/j.jml.2006.03.008 · Cited by 302 · see citing works
Norms of valence, arousal, dominance, and age of acquisition for 4,300 Dutch words
Moors, A., Houwer, J. D., Hermans, D., Wanmaker, S., van Schie, K., van Harmelen, A., Schryver, M. D., Winne, J. D., & Brysbaert, M. (2012) · Behavior Research Methods · doi:10.3758/s13428-012-0243-8 · Cited by 297 · see citing works
Self-citationThe French Lexicon Project: Lexical decision data for 38,840 French words and 38,840 pseudowords
Ferrand, L., New, B., Brysbaert, M., Keuleers, E., Bonin, P., Méot, A., Augustinova, M., & Pallier, C. (2010) · Behavior Research Methods · doi:10.3758/brm.42.2.488 · Cited by 283 · see citing works
Self-citationAdding part-of-speech information to the SUBTLEX-US word frequencies
Brysbaert, M., New, B., & Keuleers, E. (2012) · Behavior Research Methods · doi:10.3758/s13428-012-0190-4 · Cited by 267 · see citing works
Self-citationInkpen, D. Z., & Association for Computing Machinery-Digital Library (2010) · Cited by 218 · see citing works
The adaptation of the Affective Norms for English Words (ANEW) for European Portuguese
Soares, A. P., Comesaña, M., Pinheiro, A. P., Simões, A., & Frade, S. (2011) · Behavior Research Methods · doi:10.3758/s13428-011-0131-7 · Cited by 207 · see citing works
Exploring lexical co-occurrence space using HiDEx
Shaoul, C., & Westbury, C. F. (2010) · Behavior Research Methods · doi:10.3758/brm.42.2.393 · Cited by 146 · see citing works
Burgess, C. (1998) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03200643 · Cited by 133 · see citing works
Emotion words affect eye fixations during reading.
Scott, G. G., O'Donnell, P. J., & Sereno, S. C. (2012) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/a0027209 · Cited by 129 · see citing works
Taboo, emotionally valenced, and emotionally neutral word norms
Janschewitz, K. (2008) · Behavior Research Methods · doi:10.3758/brm.40.4.1065 · Cited by 122 · see citing works
Positive words carry less information than negative words
García, D., Garas, A., & Schweitzer, F. (2012) · EPJ Data Science · doi:10.1140/epjds3 · Cited by 120 · see citing works
Sensory experience ratings for over 5,000 mono- and disyllabic words
Juhasz, B. J., & Yap, M. J.-M. (2012) · Behavior Research Methods · doi:10.3758/s13428-012-0242-9 · Cited by 119 · see citing works
Imageability estimates for 3,000 disyllabic words
Schock, J., Cortese, M. J., & Khanna, M. M. (2011) · Behavior Research Methods · doi:10.3758/s13428-011-0162-0 · Cited by 119 · see citing works
Positivity of the English Language
Kloumann, I. M., Danforth, C. M., Harris, K. D., Bliss, C. A., & Dodds, P. S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029484 · Cited by 118 · see citing works
Verona, E., Sprague, J., & Sadeh, N. (2012) · Journal of Abnormal Psychology · doi:10.1037/a0025308 · Cited by 118 · see citing works
Body—object interaction ratings for 1,618 monosyllabic nouns
Tillotson, S. M., Siakaluk, P. D., & Pexman, P. M. (2008) · Behavior Research Methods · doi:10.3758/brm.40.4.1075 · Cited by 111 · see citing works
A Positivity Bias in Written and Spoken English and Its Moderation by Personality and Gender
Augustine, A. A., Mehl, M. R., & Larsen, R. J. (2011) · Social Psychological and Personality Science · doi:10.1177/1948550611399154 · Cited by 107 · see citing works
Affective norms for 210 British English and Finnish nouns
Eilola, T. M., & Havelka, J. (2010) · Behavior Research Methods · doi:10.3758/brm.42.1.134 · Cited by 102 · see citing works
Tangible Words are Recognized Faster: The Grounding of Meaning in Sensory and Perceptual Systems
Juhasz, B. J., Yap, M. J.-M., Dicke, J., Taylor, S. C., & Gullick, M. M. (2011) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2011.605150 · Cited by 92 · see citing works
Checking and bootstrapping lexical norms by means of word similarity indexes
Bestgen, Y., & Vincze, N. (2012) · Behavior Research Methods · doi:10.3758/s13428-012-0195-z · Cited by 75 · see citing works
Perceptual and motor attribute ratings for 559 object concepts
Amsel, B. D., Urbach, T. P., & Marta Kutas, A. (2012) · Behavior Research Methods · doi:10.3758/s13428-012-0215-z · Cited by 67 · see citing works
Does Response Scale Format Affect the Answering of Personality Scales?
Rammstedt, B., & Krebs, D. (2007) · European Journal of Psychological Assessment · doi:10.1027/1015-5759.23.1.32 · Cited by 62 · see citing works
Contextual self-organizing map: software for constructing semantic representations
Zhao, X., Li, P., & Kohonen, T. (2010) · Behavior Research Methods · doi:10.3758/s13428-010-0042-z · Cited by 21 · see citing works
Validating an interlingual metanorm for emotional analysis of texts
Leveau, N., Jhean‐Larose, S., Denhière, G., & Nguyen, B.-L. (2012) · Behavior Research Methods · doi:10.3758/s13428-012-0208-y · Cited by 20 · see citing works
Brems, C., Johnson, M. E., Warner, T. D., & Roberts, L. W. (2010) · Journal of HIV/AIDS & Social Services · doi:10.1080/15381501.2010.525480 · Cited by 19 · see citing works
Emotional Nouns Affect Attachment Decisions in Sentence Completion Tasks
Fraga, I., Piñeiro, A., Acuña-Fariña, C., Redondo, J., & García-Orza, J. (2012) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2012.662989 · Cited by 15 · see citing works
Flagged first, then by citations
1 of the 48 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The mea- surement of meaning. Urbana, IL: University of Illinois Press.
Cited by
Sorted by citations
Concreteness ratings for 40 thousand generally known English word lemmas
Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014) · Behavior Research Methods · doi:10.3758/s13428-013-0403-5 · Cited by 1,821 · see citing works
Self-citationAn overview of online fake news: Characterization, detection, and discussion
Zhang, X., & Ghorbani, A. A. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.004 · Cited by 997 · see citing works
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Varol, O., Ferrara, E., Davis, C., Menczer, F., & Flammini, A. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14871 · Cited by 928 · see citing works
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words
Mohammad, S. M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1017 · Cited by 616 · see citing works
A survey of multimodal sentiment analysis
Soleymani, M., García, D., Jou, B., Schuller, B. W., Chang, S., & Pantić, M. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.08.003 · Cited by 620 · see citing works
Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model
Wang, J., Yu, L.-C., Lai, K. R., & Zhang, X.-J. (2016) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p16-2037 · Cited by 527 · see citing works
SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods
Ribeiro, F. N., Araújo, M., Gonçalves, P., André Gonçalves, M., & Benevenuto, F. (2016) · EPJ Data Science · doi:10.1140/epjds/s13688-016-0085-1 · Cited by 468 · see citing works
Emotion and language: Valence and arousal affect word recognition.
Kuperman, V., Estes, Z., Brysbaert, M., & Warriner, A. B. (2014) · Journal of Experimental Psychology General · doi:10.1037/a0035669 · Cited by 427 · see citing works
Self-citationSemantic reconstruction of continuous language from non-invasive brain recordings
Tang, J., LeBel, A., Jain, S., & Huth, A. G. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01304-9 · Cited by 442 · see citing works
Mohammad, S. M. (2016) · Emotion Measurement · doi:10.1016/b978-0-08-100508-8.00009-6 · Cited by 391 · see citing works
How social learning amplifies moral outrage expression in online social networks
Brady, W. J., McLoughlin, K., Doan, T. N., & Crockett, M. J. (2021) · Science Advances · doi:10.1126/sciadv.abe5641 · Cited by 375 · see citing works
Rajadesingan, A., Zafarani, R., & Liu, H. (2015) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2684822.2685316 · Cited by 365 · see citing works
Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora
Hamilton, W. L., Clark, K., Leskovec, J., & Jurafsky, D. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1057 · Cited by 361 · see citing works
Contextualized Sarcasm Detection on Twitter
Bamman, D., & Smith, N. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v9i1.14655 · Cited by 352 · see citing works
Siew, C. S. Q., Wulff, D. U., Beckage, N., & Kenett, Y. N. (2019) · Complexity · doi:10.1155/2019/2108423 · Cited by 327 · see citing works
Generative Adversarial Networks for face generation: A survey
Kammoun, A., Slama, R., Tabia, H., Ouni, T., & Abid, M. (2022) · ACM Computing Surveys · doi:10.1145/1122445.1122456 · Cited by 326 · see citing works
The adaptation of the Affective Norms for English Words (ANEW) for Italian
Montefinese, M., Ambrosini, E., Fairfield, B., & Mammarella, N. (2013) · Behavior Research Methods · doi:10.3758/s13428-013-0405-3 · Cited by 311 · see citing works
Automatic cyberbullying detection: A systematic review
Rosa, H., Pereira, N., Ribeiro, R., Ferreira, P. C., Carvalho, J. P., Oliveira, S., Coheur, L., Paulino, P., Veiga Simão, A. M., & Trancoso, I. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.12.021 · Cited by 307 · see citing works
Challenges in building intelligent open-domain dialog systems
Huang, M., Zhu, X., & Gao, J. (2020) · ACM Transactions on Information Systems · doi:10.1145/3383123 · Cited by 288 · see citing works
A survey of state-of-the-art approaches for emotion recognition in text
Alswaidan, N., & Menaï, M. E. B. (2020) · Knowledge and Information Systems · doi:10.1007/s10115-020-01449-0 · Cited by 279 · see citing works
WASSA-2017 Shared Task on Emotion Intensity
Mohammad, S. M., & Bravo-Márquez, F. (2017) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w17-5205 · Cited by 284 · see citing works
Intentions to use bike-sharing for holiday cycling: An application of the Theory of Planned Behavior
Kaplan, S., Manca, F., Nielsen, T. A. S., & Prato, C. G. (2014) · Tourism Management · doi:10.1016/j.tourman.2014.08.017 · Cited by 263 · see citing works
Keuleers, E., Stevens, M., Mandera, P., & Brysbaert, M. (2015) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2015.1022560 · Cited by 261 · see citing works
Self-citationThe Glasgow Norms: Ratings of 5,500 words on nine scales
Scott, G. G., Keitel, A., Becirspahic, M., Yao, B., & Sereno, S. C. (2019) · Behavior Research Methods · doi:10.3758/s13428-018-1099-3 · Cited by 237 · see citing works
Buechel, S., & Hahn, U. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2092 · Cited by 256 · see citing works
Crossley, S. A., Kyle, K. D., & McNamara, D. S. (2016) · Behavior Research Methods · doi:10.3758/s13428-016-0743-z · Cited by 239 · see citing works
Four not six: Revealing culturally common facial expressions of emotion.
Jack, R. E., Sun, W. P., Delis, I., Garrod, O., & Schyns, P. G. (2016) · Journal of Experimental Psychology General · doi:10.1037/xge0000162 · Cited by 213 · see citing works
Norms of age of acquisition and concreteness for 30,000 Dutch words
Brysbaert, M., Stevens, M., Deyne, S. D., Voorspoels, W., & Storms, G. (2014) · Acta Psychologica · doi:10.1016/j.actpsy.2014.04.010 · Cited by 212 · see citing works
Self-citationAcquisition of abstract concepts is influenced by emotional valence
Ponari, M., Norbury, C., & Vigliocco, G. (2017) · Developmental Science · doi:10.1111/desc.12549 · Cited by 210 · see citing works
The Positive Effect of Not Following Others on Social Media
Valsesia, F., Proserpio, D., & Nunes, J. C. (2020) · Journal of Marketing Research · doi:10.1177/0022243720915467 · Cited by 206 · see citing works
Social signals and algorithmic trading of Bitcoin
García, D., & Schweitzer, F. (2015) · Royal Society Open Science · doi:10.1098/rsos.150288 · Cited by 209 · see citing works
Interoception: the forgotten modality in perceptual grounding of abstract and concrete concepts
Connell, L., Lynott, D., & Banks, B. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2017.0143 · Cited by 206 · see citing works
Bastick, Z. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106633 · Cited by 206 · see citing works
Variability and Consistency in Early Language Learning: The Wordbank Project
Frank, M. C., Braginsky, M., Yurovsky, D., & Marchman, V. A. (2021) · doi:10.7551/mitpress/11577.001.0001 · Cited by 206 · see citing works
Baayen, R., Chuang, Y.-Y., Shafaei-Bajestan, E., & Blevins, J. (2019) · Complex · doi:10.1155/2019/4895891 · Cited by 196 · see citing works
Affective Neural Response Generation
Asghar, N., Poupart, P., Hoey, J., Jiang, X., & Mou, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-76941-7_12 · Cited by 203 · see citing works
Emotion schemas are embedded in the human visual system
Kragel, P. A., Reddan, M. C., LaBar, K. S., & Wager, T. D. (2019) · Science Advances · doi:10.1126/sciadv.aaw4358 · Cited by 191 · see citing works
Norms of valence and arousal for 14,031 Spanish words
Stadthagen-González, H., Imbault, C., Sánchez, M. Á. P., & Brysbaert, M. (2016) · Behavior Research Methods · doi:10.3758/s13428-015-0700-2 · Cited by 201 · see citing works
Self-citationAffective Image Content Analysis: Two Decades Review and New Perspectives
Zhao, S., Yao, X., Yang, J., Jia, G., Ding, G., Chua, T., Schuller, B. W., & Keutzer, K. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3094362 · Cited by 204 · see citing works
Affective neurolinguistics: towards a framework for reconciling language and emotion
Hinojosa, J. A., Moreno, E. M., & Ferré, P. (2019) · Language Cognition and Neuroscience · doi:10.1080/23273798.2019.1620957 · Cited by 195 · see citing works
Zhao, S., Yao, H., Gao, Y., Ji, R., & Ding, G. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2617741 · Cited by 193 · see citing works
Refining Word Embeddings for Sentiment Analysis
Yu, L.-C., Wang, J., Lai, K.-H. R., & Zhang, X. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1056 · Cited by 195 · see citing works
Tan, C., Niculae, V., Danescu-Niculescu-Mizil, C., & Lee, L. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883081 · Cited by 184 · see citing works
Frequency in lexical processing
Baayen, R., Milin, P., & Ramscar, M. (2016) · Aphasiology · doi:10.1080/02687038.2016.1147767 · Cited by 134 · see citing works
Mohammad, S. M. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/58igjzkfg9k5 · Cited by 179 · see citing works
Predicting Personalized Image Emotion Perceptions in Social Networks
Zhao, S., Yao, H., Gao, Y., Ding, G., & Chua, T. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2628787 · Cited by 175 · see citing works
Refining Word Embeddings Using Intensity Scores for Sentiment Analysis
Yu, L.-C., Wang, J., Lai, K.-H. R., & Zhang, X. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2788182 · Cited by 177 · see citing works
Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation
Kiritchenko, S., & Mohammad, S. M. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2074 · Cited by 174 · see citing works
Novel Audio Features for Music Emotion Recognition
Panda, R., Malheiro, R., & Paiva, R. P. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2820691 · Cited by 173 · see citing works
Consistency and Variability in Children’s Word Learning Across Languages
Braginsky, M., Yurovsky, D., Marchman, V. A., & Frank, M. C. (2019) · Open Mind · doi:10.1162/opmi_a_00026 · Cited by 173 · see citing works
Attentional capture helps explain why moral and emotional content go viral.
Brady, W. J., Gantman, A. P., & Van Bavel, J. J. (2020) · Journal of Experimental Psychology: General · doi:10.1037/xge0000673 · Cited by 166 · see citing works
Winter, B. (2019) · Converging evidence in language and communication research · doi:10.1075/celcr.20 · Cited by 169 · see citing works
Modelling Valence and Arousal in Facebook posts
Preotiuc-Pietro, D., Schwartz, H. A., Park, G. J., Eichstaedt, J., Kern, M. L., Ungar, L., & Shulman, E. (2016) · WASSA@NAACL-HLT · doi:10.18653/v1/w16-0404 · Cited by 187 · see citing works
Why Good Is More Alike Than Bad: Processing Implications
Alves, H., Koch, A. S., & Unkelbach, C. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.12.006 · Cited by 164 · see citing works
Using a stacked residual LSTM model for sentiment intensity prediction
Wang, J., Peng, B., & Zhang, X. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.09.049 · Cited by 157 · see citing works
Embodiment in Virtual Reality Intensifies Emotional Responses to Virtual Stimuli
Gall, D., Roth, D., Stauffert, J.-P., Zarges, J., & Latoschik, M. E. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.674179 · Cited by 159 · see citing works
Iconicity in Word Learning and Beyond: A Critical Review
Nielsen, A., & Dingemanse, M. (2020) · Language and Speech · doi:10.1177/0023830920914339 · Cited by 150 · see citing works
Stigma: how it affects the substance use disorder patient
Zwick, J., Appleseth, H. S., & Arndt, S. V. (2020) · Substance Abuse Treatment Prevention and Policy · doi:10.1186/s13011-020-00288-0 · Cited by 150 · see citing works
Metaphoria: An Algorithmic Companion for Metaphor Creation
Gero, K. I., & Chilton, L. B. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300526 · Cited by 146 · see citing works
We Feel: Mapping Emotion on Twitter
Larsen, M. E., Boonstra, T. W., Batterham, P. J., O’Dea, B., Paris, C. L., & Christensen, H. M. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2403839 · Cited by 148 · see citing works
Liebenthal, E., Silbersweig, D., & Stern, E. R. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00506 · Cited by 144 · see citing works
Imbir, K. K. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01081 · Cited by 146 · see citing works
Emotionally Intelligent Chatbots: A Systematic Literature Review
Bilquise, G., Ibrahim, S., & Shaalan, K. F. (2022) · Human Behavior and Emerging Technologies · doi:10.1155/2022/9601630 · Cited by 170 · see citing works
An Analysis of Annotated Corpora for Emotion Classification in Text
Bostan, L. A. M., & Klinger, R. (2018) · International Conference on Computational Linguistics · Cited by 144 · see citing works
Mehta, A., Niles, A. N., Vargas, J. H., Marafon, T., Couto, D. D., & Gross, J. J. (2021) · Journal of Medical Internet Research · doi:10.2196/26771 · Cited by 143 · see citing works
Winter, B. (2016) · Language Cognition and Neuroscience · doi:10.1080/23273798.2016.1193619 · Cited by 140 · see citing works
The Enhancing Versus Backfiring Effects of Positive Emotion in Consumer Reviews
Rocklage, M. D., & Fázio, R. H. (2020) · Journal of Marketing Research · doi:10.1177/0022243719892594 · Cited by 142 · see citing works
Tree-Structured Regional CNN-LSTM Model for Dimensional Sentiment Analysis
Wang, J., Yu, L.-C., Lai, K.-H. R., & Zhang, X. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2959251 · Cited by 142 · see citing works
Spanish norms for affective and lexico-semantic variables for 1,400 words
Guasch, M., Ferré, P., & Fraga, I. (2015) · Behavior Research Methods · doi:10.3758/s13428-015-0684-y · Cited by 134 · see citing works
Balagopalan, A., Eyre, B., Rudzicz, F., & Novikova, J. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2557 · Cited by 144 · see citing works
Context-aware experience sampling reveals the scale of variation in affective experience
Hoemann, K., Khan, Z., Feldman, M. J., Nielson, C., Devlin, M., Dy, J., Barrett, L. F., Wormwood, J. B., & Quigley, K. S. (2020) · Scientific Reports · doi:10.1038/s41598-020-69180-y · Cited by 124 · see citing works
Early detection of promoted campaigns on social media
Varol, O., Ferrara, E., Menczer, F., & Flammini, A. (2017) · EPJ Data Science · doi:10.1140/epjds/s13688-017-0111-y · Cited by 130 · see citing works
Hinojosa, J. A., Martínez-García, N., Villalba-García, C., Fernández‐Folgueiras, U., Sánchez‐Carmona, A. J., Pozo, M. A., & Montoro, P. R. (2015) · Behavior Research Methods · doi:10.3758/s13428-015-0572-5 · Cited by 128 · see citing works
Quantifying subjective data using online Q-methodology software
Lutfallah, S., & Buchanan, L. (2019) · The Mental Lexicon · doi:10.1075/ml.20002.lut · Cited by 128 · see citing works
The principals of meaning: Extracting semantic dimensions from co-occurrence models of semantics
Hollis, G., & Westbury, C. F. (2016) · Psychonomic Bulletin & Review · doi:10.3758/s13423-016-1053-2 · Cited by 124 · see citing works
Implementing Smart Urban Metabolism in the Stockholm Royal Seaport: Smart City SRS
Shahrokni, H., Årman, L., Lazarevic, D., Nilsson, A., & Brandt, N. (2015) · Journal of Industrial Ecology · doi:10.1111/jiec.12308 · Cited by 123 · see citing works
SentiWords: Deriving a High Precision and High Coverage Lexicon for Sentiment Analysis
Gatti, L., Guerini, M., & Turchi, M. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2476456 · Cited by 121 · see citing works
Islam, S., & Zibran, M. F. (2018) · Journal of Systems and Software · doi:10.1016/j.jss.2018.08.030 · Cited by 121 · see citing works
(2020) · Advances in Experimental Social Psychology · doi:10.1016/bs.aesp.2020.04.005 · Cited by 112 · see citing works
The Massive Auditory Lexical Decision (MALD) database
Tucker, B. V., Brenner, D., Danielson, D. K., Kelley, M. C., Nenadić, F., & Sims, M. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1056-1 · Cited by 115 · see citing works
Survey on bimodal speech emotion recognition from acoustic and linguistic information fusion
Atmaja, B. T., Sasou, A., & Akagi, M. (2022) · Speech Communication · doi:10.1016/j.specom.2022.03.002 · Cited by 119 · see citing works
Keuleers, E., & Balota, D. A. (2015) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2015.1051065 · Cited by 115 · see citing works
Predicting Personalized Emotion Perceptions of Social Images
Zhao, S., Yao, H., Gao, Y., Ji, R., Xie, W., Jiang, X., & Chua, T. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2964289 · Cited by 114 · see citing works
Iacozza, S., Costa, A., & Duñabeitia, J. A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0186027 · Cited by 114 · see citing works
Extrapolating human judgments from skip-gram vector representations of word meaning
Hollis, G., Westbury, C. F., & Lefsrud, L. (2016) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2016.1195417 · Cited by 113 · see citing works
Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best–Worst Scaling
Kiritchenko, S., & Mohammad, S. M. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1095 · Cited by 113 · see citing works
Positive emotion enhances association-memory.
Madan, C. R., Scott, S. M. E., & Kensinger, E. A. (2018) · Emotion · doi:10.1037/emo0000465 · Cited by 113 · see citing works
A Textual Analysis of US Corporate Social Responsibility Reports
Clarkson, P. M., Ponn, J., Richardson, G. D., Rudzicz, F., Tsang, A., & Wang, J. (2020) · Abacus · doi:10.1111/abac.12182 · Cited by 113 · see citing works
If you want to develop an effective autism training, ask autistic students to help you
Gillespie-Lynch, K., Bisson, J. B., Saade, S., Obeid, R., Kofner, B., Harrison, A. J., Daou, N., Tricarico, N., Santos, J. D., Pinkava, W., et al. (2021) · Autism · doi:10.1177/13623613211041006 · Cited by 101 · see citing works
Rating norms should be calculated from cumulative link mixed effects models
Taylor, J. E., Rousselet, G. A., Scheepers, C., & Sereno, S. C. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01814-7 · Cited by 126 · see citing works
Consumer Responses to Corporate Social Responsibility (CSR) Contribution Type
Hildebrand, D. F. N., DeMotta, Y., Sen, S., & Valenzuela, A. (2017) · Journal of Consumer Research · doi:10.1093/jcr/ucx063 · Cited by 108 · see citing works
Kirsch, L. P., Krahé, C., Blom, N., Crucianelli, L., Moro, V., Jenkinson, P. M., & Fotopoulou, A. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.05.024 · Cited by 105 · see citing works
Troche, J., Crutch, S. J., & Reilly, J. J. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.01787 · Cited by 110 · see citing works
Ortu, M., Uras, N., Conversano, C., Bartolucci, S., & Destefanis, G. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116804 · Cited by 110 · see citing works
Sentence processing in anterior superior temporal cortex shows a social-emotional bias
Mellem, M. S., Jasmin, K., Peng, C., & Martin, A. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.06.019 · Cited by 78 · see citing works
Characterizing the 2016 Russian IRA influence campaign
Badawy, A., Addawood, A., Lerman, K., & Ferrara, E. (2019) · Social Network Analysis and Mining · doi:10.1007/s13278-019-0578-6 · Cited by 109 · see citing works
Weighing up the evidence for sound symbolism: Distributional properties predict cue strength
Westbury, C. F., Hollis, G., Sidhu, D. M., & Pexman, P. M. (2017) · Journal of Memory and Language · doi:10.1016/j.jml.2017.09.006 · Cited by 106 · see citing works
The Evaluative Lexicon 2.0: The measurement of emotionality, extremity, and valence in language
Rocklage, M. D., Rucker, D. D., & Nordgren, L. F. (2017) · Behavior Research Methods · doi:10.3758/s13428-017-0975-6 · Cited by 103 · see citing works
The relationship between implicit intergroup attitudes and beliefs
Kurdi, B., Mann, T. C., Charlesworth, T. E. S., & Banaji, M. R. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820240116 · Cited by 106 · see citing works
Second Language Feedback Abolishes the “Hot Hand” Effect during Even-Probability Gambling
Gao, S., Zika, O., Rogers, R. D., & Thierry, G. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.3622-14.2015 · Cited by 129 · see citing works
- Authors
- Warriner, A. B., Kuperman, V., & Brysbaert, M.
- Affiliations
- McMaster University (CA)
- Ghent University (BE)
- Corresponding author
- Kuperman, Victor
- Venue
- Behavior Research Methods
- Published
- 2013-02-13
- Volume
- 45
- Issue
- 4
- Pages
- 1191-1207
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W2023736093
- Cited by
- 1,959
- References
- 48
- Citations by year
- 2026: 138 · 2025: 140 · 2024: 194 · 2023: 199 · 2022: 199 · 2021: 238 · 2020: 187 · 2019: 180 · 2018: 155 · 2017: 110 · 2016: 102 · 2015: 70 · 2014: 37 · 2013: 9
- FWCI
- 80.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Behavior Research Methods (publishedVersion)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- MacSphere (McMaster University) (submittedVersion)
- MacSphere (McMaster University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Keywords
- valence, arousal, dominance, ANEW, emotional words, semantic memory, word recognition, sentiment analysis, gender differences, age differences, educational differences, taboo words
- MeSH terms
- Adolescent, Adult, Affect, Aged, Aged, 80 and over, Arousal, Educational Status, Emotions, Female, Humans, Male, Middle Aged, Reading, Reference Values, Semantics, Sesquiterpenes, Sex Characteristics, Social Dominance, Socioeconomic Factors, Vocabulary, Word Association Tests, Databases, Factual, White People, Young Adult, Crowdsourcing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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