The data is also available on Kaggle: https://www.kaggle.com/jswicker/the-smell-of-fear
The Smell of Fear data set. Information on the data set are available in
@inproceedings{wicker2015cinema,
title = {Cinema Data Mining: The Smell of Fear},
author = {Jörg Wicker and Nicolas Krauter and Bettina Derstorff and Christof Stönner and Efstratios Bourtsoukidis and Thomas Klüpfel and Jonathan Williams and Stefan Kramer},
url = {http://doi.acm.org/10.1145/2783258.2783404},
doi = {10.1145/2783258.2783404},
isbn = {978-1-4503-3664-2},
year = {2015},
date = {2015-08-13},
booktitle = {Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1235-1304},
publisher = {ACM},
address = {New York, NY, USA},
organization = {ACM},
series = {KDD '15},
abstract = {While the physiological response of humans to emotional events or stimuli is well-investigated for many modalities (like EEG, skin resistance, ...), surprisingly little is known about the exhalation of so-called Volatile Organic Compounds (VOCs) at quite low concentrations in response to such stimuli. VOCs are molecules of relatively small mass that quickly evaporate or sublimate and can be detected in the air that surrounds us. The paper introduces a new field of application for data mining, where trace gas responses of people reacting on-line to films shown in cinemas (or movie theaters) are related to the semantic content of the films themselves. To do so, we measured the VOCs from a movie theatre over a whole month in intervals of thirty seconds, and annotated the screened films by a controlled vocabulary compiled from multiple sources. To gain a better understanding of the data and to reveal unknown relationships, we have built prediction models for so-called forward prediction (the prediction of future VOCs from the past), backward prediction (the prediction of past scene labels from future VOCs) and for some forms of abductive reasoning and Granger causality. Experimental results show that some VOCs and some labels can be predicted with relatively low error, and that hints for causality with low p-values can be detected in the data.},
keywords = {atmospheric chemistry, breath analysis, causality, cinema data mining, emotional response analysis, movie analysis, time series},
pubstate = {published},
tppubtype = {inproceedings}
}
Please cite this paper when using the data set in your research. If you have any questions, feel free to contact js@wicker.nz.