@inproceedings{schlor-etal-2020-improving,
title = "Improving Sentiment Analysis with Biofeedback Data",
author = {Schl{\"o}r, Daniel and
Zehe, Albin and
Kobs, Konstantin and
Veseli, Blerta and
Westermeier, Franziska and
Br{\"u}bach, Larissa and
Roth, Daniel and
Latoschik, Marc Erich and
Hotho, Andreas},
editor = "Paggio, Patrizia and
Gatt, Albert and
Klinger, Roman",
booktitle = "Proceedings of LREC2020 Workshop ``People in language, vision and the mind'' (ONION2020)",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/2020.onion-1.5/",
pages = "28--33",
language = "eng",
ISBN = "979-10-95546-70-2",
abstract = "Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level."
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<abstract>Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level.</abstract>
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%0 Conference Proceedings
%T Improving Sentiment Analysis with Biofeedback Data
%A Schlör, Daniel
%A Zehe, Albin
%A Kobs, Konstantin
%A Veseli, Blerta
%A Westermeier, Franziska
%A Brübach, Larissa
%A Roth, Daniel
%A Latoschik, Marc Erich
%A Hotho, Andreas
%Y Paggio, Patrizia
%Y Gatt, Albert
%Y Klinger, Roman
%S Proceedings of LREC2020 Workshop “People in language, vision and the mind” (ONION2020)
%D 2020
%8 May
%I European Language Resources Association (ELRA)
%C Marseille, France
%@ 979-10-95546-70-2
%G eng
%F schlor-etal-2020-improving
%X Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level.
%U https://aclanthology.org/2020.onion-1.5/
%P 28-33
Markdown (Informal)
[Improving Sentiment Analysis with Biofeedback Data](https://aclanthology.org/2020.onion-1.5/) (Schlör et al., ONION 2020)
ACL
- Daniel Schlör, Albin Zehe, Konstantin Kobs, Blerta Veseli, Franziska Westermeier, Larissa Brübach, Daniel Roth, Marc Erich Latoschik, and Andreas Hotho. 2020. Improving Sentiment Analysis with Biofeedback Data. In Proceedings of LREC2020 Workshop "People in language, vision and the mind" (ONION2020), pages 28–33, Marseille, France. European Language Resources Association (ELRA).