Sentiment Analysis of Primary Historical Sources

Andrea Nanetti; John Pavlopoulos; Erik Cambria

doi:10.1109/ICDMW60847.2023.00104

Sentiment Analysis of Primary Historical Sources

Andrea Nanetti^*, John Pavlopoulos, Erik Cambria

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Sentiment analysis is a research area that has experienced considerable growth over the last decade. The scope of sentiment analysis is to extract feelings, opinions, and emotions from text. This paper employs state-of-the-art multilingual sentiment analysis algorithms in the field of primary historical sources to assist historians in discovering new insights. In particular, we use Sentic APIs, a neurosymbolic AI toolkit, to process the travel accounts of Ibn Battuta (in Arabic), Marco Polo (in Italian), and Ma Huan (in Chinese), available in the Engineering Historical Memory web application. Results show that sentiment analysis can be a powerful tool for historians and researchers to extract valuable insights from historical resources, shedding light on the emotions, opinions, and societal changes that shaped the past.

Original language	English
Title of host publication	Proceedings - 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023
Editors	Jihe Wang, Yi He, Thang N. Dinh, Christan Grant, Meikang Qiu, Witold Pedrycz
Publisher	IEEE Computer Society
Pages	767-772
Number of pages	6
ISBN (Electronic)	9798350381641
DOIs	https://doi.org/10.1109/ICDMW60847.2023.00104
Publication status	Published - 2023
Externally published	Yes
Event	23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023 - Shanghai, China Duration: Dec 1 2023 → Dec 4 2023

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Conference

Conference	23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023
Country/Territory	China
City	Shanghai
Period	12/1/23 → 12/4/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

ASJC Scopus Subject Areas

Computer Science Applications
Software

Keywords

Digital History
Ibn Battuta
Ma Huan
Marco Polo
Primary Historical Sources
Sentiment Analysis

Access to Document

10.1109/ICDMW60847.2023.00104

Cite this

Nanetti, A., Pavlopoulos, J., & Cambria, E. (2023). Sentiment Analysis of Primary Historical Sources. In J. Wang, Y. He, T. N. Dinh, C. Grant, M. Qiu, & W. Pedrycz (Eds.), Proceedings - 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023 (pp. 767-772). (IEEE International Conference on Data Mining Workshops, ICDMW). IEEE Computer Society. https://doi.org/10.1109/ICDMW60847.2023.00104

Nanetti, Andrea ; Pavlopoulos, John ; Cambria, Erik. / Sentiment Analysis of Primary Historical Sources. Proceedings - 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023. editor / Jihe Wang ; Yi He ; Thang N. Dinh ; Christan Grant ; Meikang Qiu ; Witold Pedrycz. IEEE Computer Society, 2023. pp. 767-772 (IEEE International Conference on Data Mining Workshops, ICDMW).

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title = "Sentiment Analysis of Primary Historical Sources",

abstract = "Sentiment analysis is a research area that has experienced considerable growth over the last decade. The scope of sentiment analysis is to extract feelings, opinions, and emotions from text. This paper employs state-of-the-art multilingual sentiment analysis algorithms in the field of primary historical sources to assist historians in discovering new insights. In particular, we use Sentic APIs, a neurosymbolic AI toolkit, to process the travel accounts of Ibn Battuta (in Arabic), Marco Polo (in Italian), and Ma Huan (in Chinese), available in the Engineering Historical Memory web application. Results show that sentiment analysis can be a powerful tool for historians and researchers to extract valuable insights from historical resources, shedding light on the emotions, opinions, and societal changes that shaped the past.",

keywords = "Digital History, Ibn Battuta, Ma Huan, Marco Polo, Primary Historical Sources, Sentiment Analysis",

author = "Andrea Nanetti and John Pavlopoulos and Erik Cambria",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023 ; Conference date: 01-12-2023 Through 04-12-2023",

year = "2023",

doi = "10.1109/ICDMW60847.2023.00104",

language = "English",

series = "IEEE International Conference on Data Mining Workshops, ICDMW",

publisher = "IEEE Computer Society",

pages = "767--772",

editor = "Jihe Wang and Yi He and Dinh, {Thang N.} and Christan Grant and Meikang Qiu and Witold Pedrycz",

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Nanetti, A, Pavlopoulos, J & Cambria, E 2023, Sentiment Analysis of Primary Historical Sources. in J Wang, Y He, TN Dinh, C Grant, M Qiu & W Pedrycz (eds), Proceedings - 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023. IEEE International Conference on Data Mining Workshops, ICDMW, IEEE Computer Society, pp. 767-772, 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023, Shanghai, China, 12/1/23. https://doi.org/10.1109/ICDMW60847.2023.00104

Sentiment Analysis of Primary Historical Sources. / Nanetti, Andrea; Pavlopoulos, John; Cambria, Erik.
Proceedings - 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023. ed. / Jihe Wang; Yi He; Thang N. Dinh; Christan Grant; Meikang Qiu; Witold Pedrycz. IEEE Computer Society, 2023. p. 767-772 (IEEE International Conference on Data Mining Workshops, ICDMW).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Sentiment Analysis of Primary Historical Sources

AU - Nanetti, Andrea

AU - Pavlopoulos, John

AU - Cambria, Erik

PY - 2023

Y1 - 2023

N2 - Sentiment analysis is a research area that has experienced considerable growth over the last decade. The scope of sentiment analysis is to extract feelings, opinions, and emotions from text. This paper employs state-of-the-art multilingual sentiment analysis algorithms in the field of primary historical sources to assist historians in discovering new insights. In particular, we use Sentic APIs, a neurosymbolic AI toolkit, to process the travel accounts of Ibn Battuta (in Arabic), Marco Polo (in Italian), and Ma Huan (in Chinese), available in the Engineering Historical Memory web application. Results show that sentiment analysis can be a powerful tool for historians and researchers to extract valuable insights from historical resources, shedding light on the emotions, opinions, and societal changes that shaped the past.

AB - Sentiment analysis is a research area that has experienced considerable growth over the last decade. The scope of sentiment analysis is to extract feelings, opinions, and emotions from text. This paper employs state-of-the-art multilingual sentiment analysis algorithms in the field of primary historical sources to assist historians in discovering new insights. In particular, we use Sentic APIs, a neurosymbolic AI toolkit, to process the travel accounts of Ibn Battuta (in Arabic), Marco Polo (in Italian), and Ma Huan (in Chinese), available in the Engineering Historical Memory web application. Results show that sentiment analysis can be a powerful tool for historians and researchers to extract valuable insights from historical resources, shedding light on the emotions, opinions, and societal changes that shaped the past.

KW - Digital History

KW - Ibn Battuta

KW - Ma Huan

KW - Marco Polo

KW - Primary Historical Sources

KW - Sentiment Analysis

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BT - Proceedings - 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023

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A2 - Qiu, Meikang

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PB - IEEE Computer Society

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Y2 - 1 December 2023 through 4 December 2023

ER -

Nanetti A, Pavlopoulos J, Cambria E. Sentiment Analysis of Primary Historical Sources. In Wang J, He Y, Dinh TN, Grant C, Qiu M, Pedrycz W, editors, Proceedings - 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023. IEEE Computer Society. 2023. p. 767-772. (IEEE International Conference on Data Mining Workshops, ICDMW). doi: 10.1109/ICDMW60847.2023.00104

Sentiment Analysis of Primary Historical Sources

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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