Article published In:
Mental Model Ascription by Intelligent AgentsEdited by Marjorie McShane
[Interaction Studies 15:3] 2014
► pp. 359–387
Edited by Marjorie McShane
[Interaction Studies 15:3] 2014
► pp. 359–387
Can you read my mindprint?
Automatically identifying mental states from language text using deeper linguistic features
Humans routinely transmit and interpret subtle information about their mental states through the language they use, even when only the language text is available. This suggests humans can utilize the linguistic signature of a mental state (its mindprint), comprised of features in the text. Once the relevant features are identified, mindprints can be used to automatically identify mental states communicated via language. We focus on the mindprints of eight mental states resulting from intentions, attitudes, and emotions, and present a mindprint-based machine learning technique to automatically identify these mental states in realistic language data. By using linguistic features that leverage available semantic, syntactic, and valence information, our approach achieves near-human performance on average and even exceeds human performance on occasion. Given this, we believe mindprints could be very valuable for intelligent systems interacting linguistically with humans. Keywords: mental state; linguistic features; mindprint; natural language processing; information extraction
References
Anand, P., King, J., Boyd-Graber, J., Wagner, E., Martell, C., Oard, D., & Resnik, P
(2011) Believe me – We can do this! Annotating persuasive acts in blog text. In
Proceedings of the AAAI Workshop on Computational Models of Natural Argument
. San Francisco, CA: AAAI.
Brown, P., & Levinson, S
(1987) Politeness: Some universals in language usage. Cambridge, MA: Cambridge University Press. 
Chaffar, S., & Inkpen, D
(2011) Using a heterogeneous dataset for emotion analysis in text. Lecture Notes in Computer Science, 66571, 62–67.
Danescu-Niculescu-Mizil, C., Sudhof, M., Jurafsky, D., Leskovec, J., & Potts, C
(2013) A computational approach to politeness with application to social factors. In
Proceedings of ACL
. Sofia, Bulgaria: ACL.
Ditta, A., & Steyvers, M
(2013) Collaborative memory in a serial combination procedure. Memory, 211, 668–674.
Fellbaum, C
(1998) WordNet: An electronic lexical database. Cambridge, MA: MIT Press.
Grice, P
(1975) Logic and conversation. In P. Cole & J. Morgan (Eds.), Syntax and semantics. 3: Speech acts (pp. 41–58). New York: Academic Press.
Griffiths, T., & Steyvers, M
(2004) Finding scientific topics. Proceedings of the National Academy of Sciences, 1011, 5228–5235.
Hacker, S., & von Ahn, L
(2009) Matchin: Eliciting user preferences with an online game. In
Proceedings of SIGCHI Conference on Human Factors in Computing Systems
(pp. 1207–1216). Boston, MA: Association for Computing Machinery.
Hardisty, E., Boyd-Graber, J., & Resnik, P
(2010) Modeling perspective using adaptor grammars. In
Proceedings of Empirical Methods in Natural Language Processing
(pp. 284–292). Boston, MA: ACL-EMNLP.
Keshtkar, F., & Inkpen, D
(2009) Using sentiment orientation features for mood classification in blogs. In
Proceedings of IEEE International Conference on Natural Language Processing and Knowledge Engineerings
(pp. 24–29). Dalian, China: IEEE.
Krishnapuram, B., Figueiredo, M., Carin, L., & Hartemink, A
(2005) Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 271, 957–968.
Kruger, J., Epley, N., Parker, J., & Ng, Z.-W
(2005) Egocentrism over e-mail: Can we communicate as well as we think? Journal of Personality and Social Psychology, 89(6), 925–936.
Law, E., & von Ahn, L
(2009) Input-agreement: A new mechanism for collecting data using human computation games. In
Proceedings of SIGCHI Conference on Human Factors in Computing Systems
(pp. 1197–1206). Boston, MA: Association for Computing Machinery.
Lee, M., Steyvers, M., de Young, M., & Miller, B
(2012) Inferring expertise in knowledge and prediction ranking tasks. Topics in Cognitive Science, 41, 151–163.
Lin, W., Wilson, T., Wiebe, J., & Hauptmann, A
(2006) Which side are you on? Identifying perspectives at the document and sentence levels. In
Proceedings of the Conference on Natural Language Learning (CoNLL)
. New York: CoNLL.
McPartland, J., & Klin, A
(2006) Asperger’s syndrome. Adolescent Medicine Clinics, 171(3), 771–88.
Mihalcea, R., & Strapparava, C
(2009) The lie detector: Explorations in the automatic recognition of deceptive language. In
Proceedings of the Association for Computational Linguistics
. Singapore: ACL.
Mishne, G
(2005) Experiments with mood classification in blog posts. In
Proceedings of SIGIR 2005
. Salvador, Brazil: ACM.
Mohammed, S
(2012) Portable features for classifying emotional text. In
Proceedings of 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
(pp. 587–591). Montreal, Canada: NAACLHLY.
Neviarouskaya, A., Prendinger, H., & Ishizuka, M
(2010) Recognition of affect, judgment, and appreciation in text. In
Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010)
(pp. 806–814). Beijing China: COLING.
Pearl, L., & Steyvers, M
(2010) Identifying emotions, intentions, & attitudes in text using a game with a purpose. In
Proceedings of NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
. Los Angeles, CA: NAACL.
(2012) Detecting authorship deception: A supervised machine learning approach using author writeprints. Literary and Linguistic Computings, 27(2), 183–196.
(2013) “C’mon – You should read this”: Automatic identification of tone from language text. International Journal of Computational Linguistics, 4(1), 12–30.
Pennebaker, J., Booth, W., & Francis, M
(2007) Linguistic inquiry and word count: Liwc. Austin, TX: LIWC.net.
Princeton-University
Rosch, E
(1978) Principles of categorization. In E. Rosch & B. Lloyd (Eds.), Cognition and categorizatio. Hillsdale: Lawrence Erlbaum Associates.
Rubin, V.L., & Conroy, N.J
(2011) Challenges in automated deception detection in computer-mediated communication. Proceedings of the American Society for Information Science and Technology, 48(1), 1–4.
Strapparava, C., & Mihalcea, R
(2008) Learning to identify emotions in text. In
Proceedings of the ACM Symposium on Applied Computing
(pp. 1556–1560). Fortaleza, Brazil: ACM.
Strapparava, C., & Valitutti, A
(2004) WordNetAffect: An affective extension of WordNet. In
Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC)
(pp. 1083–1086). Lisbon, Portugal: LREC.
Toutanova, K., Klein, D., Manning, C., & Singer, Y
(2003) Feature-rich part-of-speech tagging with a cyclic dependency network. In
Proceedings of HLT-NAACL 2003
(pp. 252–259). Edmonton, Canada: HLT-NAACL.
von Ahn, L
(2006) Games with a purpose. IEEE Computer Magazine, June1, 96–98.
von Ahn, L., & Dabbish, L
(2004) Labeling images with a computer game. In
Proceedings of SIGCHI Conference on Human Factors in Computing Systems
(pp. 219–326). Vienna, Austria: Association for Computing Machinery.
von Ahn, L., Kedia, M., & Blum, M
(2006) Verbosity: A game for collecting common-sense facts. In
Proceedings of SIGCHI Conference on Human Factors in Computing Systems
(pp. 75–78). New York, NY: Association for Computing Machinery.
von Ahn, L., Liu, R., & Blum, M
(2006) Peekaboom: A game for locating objects in images. In
Proceedings of SIGCHI Conference on Human Factors in Computing Systems
(pp. 55–64). New York, NY: Association for Computing Machinery.
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.
Yi, S., Steyvers, M., & Lee, M
(2012) The wisdom of crowds in combinatorial problems. Cognitive Science, 36(3), 452–470.
Cited by
Cited by 1 other publications
Vogler, Nikolai & Lisa Pearl
2020. Using linguistically defined specific details to detect deception across domains. Natural Language Engineering 26:3 ► pp. 349 ff. 
This list is based on CrossRef data as of 31 march 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.