Abstrakti
Our paper analyses emotional language used by Finnish soldiers and civilians in their private communication during World War II. The dataset consists of 7,000 handwritten letters converted into a machine-readable corpus with rich metadata. The dataset offers a unique opportunity to make a statistical analysis of people’s emotional responses to the war. We engage in key questions of the cultural history of war, such as the connection between soldiers’ emotional language and violence: did soldiers’ emotional language become more laconic in the course of the war? While computational approaches to mining emotions have been common in fields like
computer science and linguistics, they have not gained wider popularity in historical research. Pioneering attempts have been based on individual emotion words carefully chosen by an historian, or on readily available, more generic emotion lexicons. Compared to machine-learning solutions, lexicon-based approaches require less computational effort and are more transparent to interpret. Our methodology combines the ready-made word list FEIL with contextual knowledge of historians. FEIL gives around 7,000 Finnish words an emotion category and intensity ratings. First, the emotion lexicon was filtered based on high intensity. Then the domain expert manually removed words not particularly emotionally intensive in the context of war letters. The expert also annotated the list of the most frequent words in the war letter collection and handpicked emotionally intensive words not included in FEIL. Our final
list covered 298 emotion words. We quantified changes in their use over time. In contrast to earlier research, our analysis indicates that soldiers’ and civilians’ emotionality did not significantly differ during World War II. Soldiers’ use of emotion words saw a decline in the last stages of the war, but overall their letters were almost as emotional as the civilians’ letters. We did indeed identify some changes in the individual emotion words used by the soldiers in their letters: patriotic words in particular decreased in the course of the war. In addition to empirical findings, our paper sheds light on the problem of universal emotion lexicons in historical research: linguistic, cultural and temporal differences between present-day lexicons and historical datasets can lead to biased interpretations. Thus, our paper contributes not only to the history of emotions but also to emotion mining, which is historically sensitive.
computer science and linguistics, they have not gained wider popularity in historical research. Pioneering attempts have been based on individual emotion words carefully chosen by an historian, or on readily available, more generic emotion lexicons. Compared to machine-learning solutions, lexicon-based approaches require less computational effort and are more transparent to interpret. Our methodology combines the ready-made word list FEIL with contextual knowledge of historians. FEIL gives around 7,000 Finnish words an emotion category and intensity ratings. First, the emotion lexicon was filtered based on high intensity. Then the domain expert manually removed words not particularly emotionally intensive in the context of war letters. The expert also annotated the list of the most frequent words in the war letter collection and handpicked emotionally intensive words not included in FEIL. Our final
list covered 298 emotion words. We quantified changes in their use over time. In contrast to earlier research, our analysis indicates that soldiers’ and civilians’ emotionality did not significantly differ during World War II. Soldiers’ use of emotion words saw a decline in the last stages of the war, but overall their letters were almost as emotional as the civilians’ letters. We did indeed identify some changes in the individual emotion words used by the soldiers in their letters: patriotic words in particular decreased in the course of the war. In addition to empirical findings, our paper sheds light on the problem of universal emotion lexicons in historical research: linguistic, cultural and temporal differences between present-day lexicons and historical datasets can lead to biased interpretations. Thus, our paper contributes not only to the history of emotions but also to emotion mining, which is historically sensitive.
Alkuperäiskieli | Englanti |
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Otsikko | Proceedings of the 6th Digital Humanities in the Nordic and Baltic Countries Conference (DHNB 2022) |
Alaotsikko | Uppsala, Sweden, March 15–18, 2022 |
Toimittajat | Karl Berglund, Matti La Mela, Inge Zwart |
Kustantaja | CEUR Workshop Proceedings |
Sivut | 135-144 |
Tila | Julkaistu - 2022 |
OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
Tapahtuma | Digital Humanities in the Nordic and Baltic Countries Conference - Uppsala, Ruotsi Kesto: 15 maalisk. 2022 → 18 maalisk. 2022 |
Julkaisusarja
Nimi | CEUR Workshop Proceedings |
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Vuosikerta | 3232 |
ISSN (elektroninen) | 1613-0073 |
Conference
Conference | Digital Humanities in the Nordic and Baltic Countries Conference |
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Maa/Alue | Ruotsi |
Kaupunki | Uppsala |
Ajanjakso | 15/03/22 → 18/03/22 |
Julkaisufoorumi-taso
- Jufo-taso 1
Sormenjälki
Sukella tutkimusaiheisiin 'Mining Emotions from the Finnish War Letter Collection, 1939–1944'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.Tietoaineistot
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Finnish War Letter Emotion Lexicon
Turunen, R. (Creator) & Taskinen, I. (Creator), Zenodo, 31 toukok. 2022
DOI - pysyväislinkki: 10.5281/zenodo.6600568
Tietoaineisto: Dataset