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{{Paper
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|title=The Geography of ‘Fear’, ‘Sadness’, ‘Anger’ and ‘Joy’: Exploring the Emotional Landscapes in the Holocaust Survivors’ Testimonies
|pdfUrl=https://ceur-ws.org/Vol-3671/paper8.pdf
|volume=Vol-3671
|authors=Ignatius Ezeani,Paul Rayson,Ian Gregory,Tim Cole,Erik Steiner,Zephyr Frank
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==The Geography of ‘Fear’, ‘Sadness’, ‘Anger’ and ‘Joy’: Exploring the Emotional Landscapes in the Holocaust Survivors’ Testimonies==
https://ceur-ws.org/Vol-3671/paper8.pdf
The Geography of ‘Fear’, ‘Sadness’, ‘Anger’ and ‘Joy’:
Exploring the Emotional Landscapes in the Holocaust
Survivors’ Testimonies
Ignatius Ezeani1,⇤ , Paul Rayson1 , Ian Gregory2 , Tim Cole3 , Erik Steiner4 and
Zephyr Frank4
1
UCREL, School of Computing and Communications, InfoLab21, Lancaster University, Lancaster, LA1 4WA, UK
2
Department of History, Lancaster University, Lancaster, LA1 4YT, UK
3
Department of History, University of Bristol, BS8 1TB, UK
4
Department of History, School of Humanities and Sciences, Stanford University, USA
Abstract
Holocaust survivors’ testimonies provide a rich source of evidence about the personal experiences of
survivors who witnessed and endured horrors during the Nazi genocide of Jews and other persecuted
groups. The narratives contain references to the emotions experienced when describing memories of
people, places, and events. Analysing the spatiality of these human emotions enables us to understand
how they are connected to the places around them. We focus on fear, sadness, anger, as well as joy to
examine the interplay of these emotional experiences by multiple individuals at different places and
times and in different circumstances. Understanding these complex emotional landscapes, especially
from very large collections of textual data requires a carefully designed technique that can effectively
and efficiently apply existing and new technologies. In this work, therefore, we explore the possibility of
extracting and analysing these emotions as well as their related geographies by applying a combination
of natural language processing methods including large language models.
Keywords
spatial narratives, holocaust testimonies, large language models, emotion geography, spatial emotion
classification
In: R. Campos, A. Jorge, A. Jatowt, S. Bhatia, M. Litvak (eds.): Proceedings of the Text2Story’24 Workshop, Glasgow,
Scotland, 24-March-2024
⇤
Corresponding author.
� i.ezeani@lancaster.ac.uk (I. Ezeani); p.rayson@lancaster.ac.uk (P. Rayson); i.gregory@lancaster.ac.uk
(I. Gregory); tim.cole@bristol.ac.uk (T. Cole); ebs110@stanford.edu (E. Steiner); ebs110@stanford.edu (Z. Frank)
� https://www.lancaster.ac.uk/scc/about-us/people/ignatius-ezeani (I. Ezeani);
https://www.lancaster.ac.uk/scc/about-us/people/paul-rayson (P. Rayson);
https://www.lancaster.ac.uk/staff/gregoryi/ (I. Gregory);
https://www.bristol.ac.uk/people/person/Tim-Cole-7e176567-189f-4f5e-b62e-c6a3e7d5e84a/ (T. Cole);
https://web.stanford.edu/group/spatialhistory/static/ (E. Steiner); https://history.stanford.edu/people/zephyr-frank
(Z. Frank)
� 0000-0001-8286-9997 (I. Ezeani); 0000-0002-1257-2191 (P. Rayson); 0000-0001-8745-2242 (I. Gregory);
0000-0001-5711-8200 (T. Cole); 0009-0001-4407-8956 (E. Steiner); 0000-0003-0691-9299 (Z. Frank)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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1. Introduction
During what became known as the Holocaust, the Nazi regime systematically persecuted and
murdered millions of Jews and other targeted groups during World War II. The survivors of
these events have shared their personal experiences and memories through various forms
of testimony, such as interviews, memoirs, and artworks. These provide valuable sources of
historical and cultural knowledge, as well as emotional and psychological insight into the
human condition under extreme circumstances. One aspect that can be explored in Holocaust
survivors’ testimonies is the spatial and temporal dimensions of the emotions expressed about
people, places, and events, otherwise known as emotional geography. As Guy Miron signals
in the case of German Jews, individuals experienced Nazi spatial control “both as a feeling
and as a physical reality” [1]. Just as spatial experiences had an emotional dimension, so too
did emotions have a spatiality or geography. Emotional geography is a concept that helps us
understand how people feel about and react to, their environment, and how their environment
influences their identity and memory. It also allows us to examine the interplay of different
emotional experiences, such as fear, anger, surprise, sadness, disgust, and even joy, by multiple
individuals at different places and times and in different situations.
However, studying the emotional geography of Holocaust survivors is not an easy task,
especially when dealing with very large collections of video, audio, and textual data (the
collection we work with includes over 55,000 recorded interviews). Therefore, there is a need
for effective methods and tools to extract and analyse the emotions and geographies from
the Holocaust survivors’ testimonies and to visualise and present the results in a meaningful
and accessible way. This work aims to address this challenge by applying a combination of
existing natural language processing (NLP) techniques, such as sentiment analysis, emotion
detection, named entity recognition, geocoding, and geovisualisation, to a corpus of video-
recorded testimonies from the USC Shoah Foundation. The key research questions that drive
this work include:
• How can we use NLP techniques - possibly leveraging large language models - to extract
and analyse expressions of emotions in Holocaust testimonies?
• How much ‘fear’ compared to other emotions - ‘sadness’, ‘anger’, and ‘joy’ - is contained
in each testimony narrative?
• How does the expression of ‘fear’ change across the narrative sequence of each testimony?
This paper presents the outcome of exploratory work which contributes::
• An application of NLP methods to the study of the emotional geography of Holocaust
survivors, and a demonstration of the potential and challenges of using these techniques
for this purpose.
• An analysis of the emotions and geographies expressed in the testimonies, and a discovery
of new patterns and insights that can enhance our understanding of the Holocaust and
its survivors.
94
2. Related Work
This work draws on and contributes to two main fields of research: emotional geography and
natural language processing. Emotional geography is an interdisciplinary field that studies
the relationship between human emotions and space, place, and environment [2]. It covers a
wide range of topics and perspectives, such as the emotional attachment to place, the emotional
impact of displacement and migration, the emotional dimensions of power and resistance, the
emotional aspects of memory and identity, and the emotional expressions of culture and society
[3].
Researchers have long advocated exploring the physicality of the event. Beorn et al. signalled
that the Holocaust was ‘rooted in specific physical spaces, times, and landscape’ and also
‘characterised by a spatiality of the process - concentration, deportation, dispersal, dislocation’
[4]. Some of their key questions were: How did one (or why would one) “map” testimony?, How
would a typological approach to the Holocaust differ from accounts of individual experience?,
How can the “cognitive mapping” so present in survivor and postwar SS tribunal testimony be
reconciled with the physical environment of landscapes and buildings? However in the 2000s,
when these ideas were being proposed, the required technology was either not available or was
not fully developed. However, multidisciplinary collaboration involving historians, historical
geographers, GIScientists, and cartographers were established [5]. Advances in AI and natural
language processing (NLP) enabled researchers to have the platform and the technology for a
deeper investigation of the transcripts of testimonies. Recent approaches apply a variety of NLP
techniques [6, 7, 8, 9] that leverage large language models (e.g GPTs) and other transformer-
based models. For example, Woods et al. [8] investigated how sentiments associated with places
vary over the narrative sequence by comparing the performance of different machine learning
algorithms. Beyond geocoding, some studies [10] applied bespoke named entity recognisers to
extract and analyse named places and other spatial elements - geographical features (‘river’,
‘hill’, ‘road’), imprecise description of landscapes (‘the majestic mountains’, ‘the camp’) and
feature relative terms (“a quick detour along the lake”, “turn left after the inn”) in text [11].
However, previous work has not used automated methods on a large-scale to annotate emotions
in testimonies.
3. Dataset and Methods
For this work, we have focused on a small portion of the Holocaust Survivors’ Testimonies (HST)
which comprises a random selection of transcripts of one thousand oral history interviews
(about 21 million words) undertaken by the USC Shoah Foundation Visual History Archive1 in
the 1990s.
3.1. Dataset: Holocaust Survivors’ Testimonies
The transcripts follow a similar format that includes a series of questions posed by the interviewer
and the corresponding answers from the interviewee who is a survivor. According to the
1
Information about the USC Shoah Foundation Visual History Archive can be found https://sfi.usc.edu/what-we-do/
collections
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Table 1
Analysis of the Holocaust Survivor’s Testimonies files and contents
Holocaust Survivor’s Testimonies
File count 1000
Sentence count 816,800
Words (tokens) count 21,516,122
File size range (words) 4852 – 84,051
Averages file size (words) 21,516
Table 2
Statistics of a sample of 10 Testimonies used in this work. The token (Tokens) and sentence (Sents)
counts focus only on pairs consisting of the responses from the survivors and the questions from the
interviewer. The IDs correspond respectively to the testimony file names
ID QA-Pairs Tokens Sents ID QA-Pairs Tokens Sents
268 98 43192 4084 37556 233 57862 4450
36999 175 12965 1250 37567 253 24182 2563
37210 254 39245 3655 37585 273 12367 1354
37250 186 41199 4223 37605 273 12555 1435
37409 132 18151 1759 37648 181 19731 1850
interview guideline [12], each interview focuses on the individual’s experiences during the
Holocaust which are explored in a broadly chronological order. Each interview – generally
of around two hours duration – devotes approximately 20 percent of the time to pre-war life,
60 percent to wartime experiences focused on the events of the Holocaust, and 20 percent to
post-war life [13]. In short, these are not full life histories, but more focused interviews asking
about wartime experiences across a series of sites of incarceration or hiding. These sites serve
as anchors in the narratives that describe survivors’ wartime trajectories.
In this exploratory work, we used only a small sample of 10 out of the 1000 testimonies
described above as a preliminary test to assess the salience of the emotion of fear, its spatial
distribution, and its relation to other important emotional states. For this experiment, we
purposely restrict our sample size and the number of emotions examined to allow for the
application of domain expert knowledge to check the consistency and accuracy of our methods.
3.2. Spatial Entity Extraction
This extraction pipeline used in this work was a version of the framework, illustrated in Figure
1, defined by [11] for extracting place names and geographical feature nouns from text through
named entity recognition for the Lake District corpora, and a more generalised version of
the framework described in another work [10]. The key elements of the framework include
processes for enhancing an off-the-shelf named-entity recogniser, Spacy [14], with lists of items
(e.g. place names, geographical feature nouns, dates and time, sentiments or emotions, locative
adverbs, and spatial prepositions). This was achieved by adding the Spacy’s rule-based module
annotation module ‘EntityRuler‘ to the models pipeline thereby improving the ability of its
96
Figure 1: An overview of the spatial entity extraction framework used in this work, originally presented
by Ezeani et al. [11, 10].
named entity recogniser to identify and extract our pre-defined spatial entities and concepts.
The enhanced model is subsequently applied to similar texts to perform surface-level extraction
of spatial elements and even sentiment-bearing words for basic sentiment analysis.
Using this technique, we could identify some key geospatial elements like the countries, cities,
concentration camps, and even geonouns mentioned in the testimonies. Table 3 shows some of
the top elements identified in the answers provided by the survivors in the 2 randomly selected
testimonies - id=‘01’ and id=‘02’. For this work however, we explore emotion beyond the binary
classifications of positive and negative sentiments to capture more fine-grained affect analysis
but restricted to mainly ‘fear’ but ‘sadness’, ‘anger’, and ‘joy’ which were also included for richer
comparative analysis. These classifications are a selection from the 6 basic classes of emotion
(‘fear’, ‘anger’, ‘disgust’, ‘joy’, ‘sadness’, ‘surprise’) identified by Ekman and Friesen [15] as well
as the ‘neutral’ class, which are popularly used in NLP for studies in affect classification and
analysis.
3.3. Emotion Classification
The transcribed testimonies are available in plain text formats generally structured for conversa-
tional turn-taking mainly between the interviewer and the survivor but often with interjection
from crew members or family members of the survivors. We first pre-processed the files to
separate the questions by the interviewer and the responses by the survivor. We then focused
only on these responses (see Table 2 for details) given by the survivor for the emotional classi-
fication. Since some of these responses were quite long and span many paragraphs, we split
them into sentences to keep the context to a manageable scope.
3.3.1. Classification Models
There was no available bespoke emotion model trained on the Holocaust corpora or a dataset
for training one that we were aware of during this work. Besides, this was meant to be an
exploratory work with no intention of building new models at the first stage. Hence, we
proceeded with existing off-the-shelf emotion classification models as well as generic large
language models. We therefore applied the following three models to classify the sentence
contexts extracted from survivors’ testimonies and select the most ‘voted’ class out of the three.
The first model we applied was Hartmann’s transformer model [16]. The model which is
a version of the DistilRoBERTa-base fine-tuned on about 20k observations extracted from six
diverse emotion datasets. The selected observations are fairly distributed across the emotion
97
Figure 2: An example of the large language model prompt used for the classification of emotions from
the Holocaust texts
Figure 3: An example of the format for the pre-processed testimonies
classes. It is a lightweight transformer model that is freely available and easy to use, hence
the choice. Another key contribution of this model is the provision of the emotion scores as a
probability distribution across the labels.
From the output of the transformer model, we retained only the contexts that were labelled
with the four classes we are studying in this paper - ‘fear’, ‘sadness’, ‘anger’, and ‘joy’. Given
the need for some form of evaluation, these classified sentences were then passed individually
to a variant of the GPT-3.5 Turbo models [17] gpt-3.5-turbo-instruct as well as the
GPT-4 model [18] with the same prompt basically to get another opinion. We tried several
prompts to see which works best but Figure 2 shows an example of the prompt we settled for
the LLM experiments.
Finally, the accepted class for each sentence is determined by selecting the most ‘voted’
class. This requires that at least two of the models will predict a particular class for it to be
selected, otherwise ‘none’. While we do not expect the approach to guarantee high-quality
annotations we consider it a stop-gap method that is good enough to enable us to gain some
interesting insights from the Holocaust testimonies.
4. Results and Discussion
We performed the classification with the models and ‘voting’ process, and confusion matrices
can be seen in Figure 6. Our final output is a set of 5,461 sentences with emotion scores and
labels for fear, sadness, anger, and joy. Table 3 shows examples of some of the sentences and
98
Figure 4: Graph showing the counts of sentences classified as one of the four key emotions - sadness,
anger, fear, joy - by different models
the scores assigned by different models while Figure 4 is a plot of the distributions of the four
emotions or ‘none’ as predicted by the models. The label ‘none’ is not present in the predictions
from roberta because we only extracted instances of the four labels from its output. As shown
in Figure 4, the emotions of ‘fear’ and ‘sadness’ tend to trend in the same pattern consistently
across the model outputs.
When comparing the variations of the negative emotions fear, sadness, anger and positive
emotion joy across the narrative sequence as shown in Figure 5, some interesting patterns
emerge. Survivors tend to express positive emotions at the beginning and end of the interviews
while negative emotions tend to follow a bimodal, increasing pattern before falling sharply
towards the end of the interviews. This follows from the structure of interviews, where survivors
begin by discussing pre-war childhood memories and end with reflections on more recent life
events such as their own children. Across all interviews, positive and negative emotions are
inversely correlated, with negative emotions of fear and sadness appearing together most often.
Finally, Table 4 shows the connections we observe in these 10 texts between named places
and the emotions observed. We split the results into three groups: 1) places, 2) geographic
feature nouns, and 3) camps, and for each group, we list the overall top five in each group, plus
the lists associated most with each of the four emotions. This serves to illustrate the potential
of the technique for linking emotions to places of the different types under analysis.
5. Conclusion
In this work, we explored the idea of developing a computational framework for analysing the
emotional landscapes of a textual narrative in a more nuanced form beyond the classification
into positive and negative sentiments which has been undertaken previously. We applied our
method to a sample of 10 Holocaust survivors’ testimonies specifically focusing on only four
out of Ekmans 6 emotion classes [15] - ‘fear’, ‘sadness’, ‘anger’, and ‘joy’. These were chosen
for no particular reason other than that they seem like sensible themes for Holocaust-related
study. The interaction of these emotions with the geography of the space they were connected
99
Figure 5: Graph showing smoothed emotion counts across the combined testimonies
Figure 6: The confusion matrix comparing the prediction agreements between the ‘voted’ labels and
those from the models.
to - named places, camps, and geographical features were also extracted and analysed.
Given that we had no available model or previously existing annotated dataset for our task,
we applied an efficient combination of models consisting of a lightweight transformer-based
model and two large language models to support our annotation process. We are aware of the
limitations of this method and will focus on building a more robust, bespoke, and in-domain
100
Table 3
Example: Sentence-level emotion scores and model labels. ‘Mod1’, ‘Mod2’, and ‘Mod3’ refer to roberta,
gpt3.5 and gpt4 respectively.
text fear sad anger joy roberta gpt3.5 gpt4 voted
1. And I used to be very afraid be- 0.9921 0.0017 0.0012 0.0011 fear fear fear fear
cause I was the only Jew on the– on
the street.
2. And at that time, I lost my mother 0.0030 0.9811 0.0023 0.0010 sad sad sad sad
and my sister and my little nephew.
3. But still, they were incensed that 0.0021 0.0030 0.9709 0.0007 anger anger anger anger
this kind of jazz would be played
there.
4. So they were relieved to see us in 0.0004 0.0037 0.0018 0.9695 joy joy joy joy
the morning, coming back home.
5. They drove him out, out of the camp, 0.0668 0.0226 0.3528 0.0072 anger fear sad none
and they said, go back.
model for more accurate labelling and analysis. However, it produced sufficiently good results
for initial exploratory research. We will fine-tune our processes and scale up our study to analyse
a larger collection of Holocaust testimonies to gain more insight into the complex interplay of
emotions in a spatio-temporal context, potentially fine-tune a model on Holocaust data, to link
emotions to those who experienced them, and to explore zero-shot learning methodologies in
the future.
Acknowledgments
We thank the anonymous reviewers for their comments on our paper submission. The project
is funded in the UK from 2022 to 2025 by ESRC, project reference: ES/W003473/1. We also
acknowledge the input and advice from the other members of the project team in generating
requirements for our research presented here and the UCREL Hex team for providing the
compute needs for this project[19]. More details of the project can be found on the website:
https://spacetimenarratives.github.io/
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Table 4
Top 5 named places (countries or cities), geographical features, and camps in the 10 testimonies compared
with places associated with each emotion. Each row is the cumulative count of the entity in the collection
followed by the percentage occurrence of the top ones.
Places
Overall (443) (Russia,11%); (Israel,7%); (Germany,6%); (York,5%); (Poland,5%)
fear (83) (Russia,23%); (Germany,9%); (Braunschweig,5%); (Palestine,5%); (Buchenwald,3%)
sadness (68) (Russia,9%); (Germany,9%); (Israel,9%); (York,7%); (Hungary,5%)
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