Chord Reviews: A Python Package for Analyzing Online Reviews

Félix Funes; Nuno António

doi:10.1007/978-3-032-20132-4_5

Chord Reviews: A Python Package for Analyzing Online Reviews

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

Abstract

User Generated Content (UGC), particularly online customer reviews, serves as a crucial source of feedback for businesses by offering insights into customer sentiments and preferences. However, these reviews’ unstructured nature and sheer volume present significant challenges in extracting actionable insights. This study addresses this challenge by proposing an innovative approach that combines data visualization and text mining techniques to optimize the discovery of insights from online reviews. Inscribed in the design science research paradigm, this study presents a novel Python package that combines chord diagrams with natural language processing (NLP) techniques to obtain insights from online reviews’ textual content. Preprocessing and sentiment analysis transform unstructured text into structured data, revealing prevalent themes and co-occurring associations. The Chord Reviews package’s resultant artifact demonstrates robust functionality, completeness, consistency, accuracy, performance, reliability, and usability across diverse domains and datasets. This package offers businesses a versatile tool for textual analysis, enabling informed decision-making to enhance customer satisfaction, product development, or market competitiveness.

Original language	English
Title of host publication	Going Back to the Roots of Marketing
Subtitle of host publication	Proceedings of the 2025 AMS Annual Conference, Montreal, Quebec, Canada, May 21-23
Editors	Jasmine Parajuli, Amrita Dey
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	43-55
Number of pages	13
ISBN (Electronic)	978-3-032-20132-4
ISBN (Print)	978-3-032-20131-7, 978-3-032-20134-8
DOIs	https://doi.org/10.1007/978-3-032-20132-4_5
Publication status	Published - 1 May 2026
Event	2025 Academy of Marketing Science World Marketing Congress - Burgundy School of Business, Dijon, France Duration: 1 Jul 2025 → 4 Jul 2025 Conference number: 26 https://www.ams-web.org/wmc-dijon-france

Publication series

Name	Developments in Marketing Science: Proceedings of the Academy of Marketing Science
Publisher	Springer
ISSN (Print)	2363-6165
ISSN (Electronic)	2363-6173

Conference

Conference	2025 Academy of Marketing Science World Marketing Congress
Abbreviated title	2025 WMC
Country/Territory	France
City	Dijon
Period	1/07/25 → 4/07/25
Internet address	https://www.ams-web.org/wmc-dijon-france

Keywords

Data visualization
Text analytics
Customer reviews
Design science research
Chord diagrams

Access to Document

10.1007/978-3-032-20132-4_5

Cite this

Funes, F., & António, N. (2026). Chord Reviews: A Python Package for Analyzing Online Reviews. In J. Parajuli, & A. Dey (Eds.), Going Back to the Roots of Marketing: Proceedings of the 2025 AMS Annual Conference, Montreal, Quebec, Canada, May 21-23 (pp. 43-55). (Developments in Marketing Science: Proceedings of the Academy of Marketing Science). Springer. https://doi.org/10.1007/978-3-032-20132-4_5

Funes, Félix ; António, Nuno. / Chord Reviews : A Python Package for Analyzing Online Reviews. Going Back to the Roots of Marketing: Proceedings of the 2025 AMS Annual Conference, Montreal, Quebec, Canada, May 21-23. editor / Jasmine Parajuli ; Amrita Dey. Cham, Switzerland : Springer, 2026. pp. 43-55 (Developments in Marketing Science: Proceedings of the Academy of Marketing Science).

@inproceedings{ff0ff73c0ace4006b37056ef6d00c751,

title = "Chord Reviews: A Python Package for Analyzing Online Reviews",

abstract = "User Generated Content (UGC), particularly online customer reviews, serves as a crucial source of feedback for businesses by offering insights into customer sentiments and preferences. However, these reviews{\textquoteright} unstructured nature and sheer volume present significant challenges in extracting actionable insights. This study addresses this challenge by proposing an innovative approach that combines data visualization and text mining techniques to optimize the discovery of insights from online reviews. Inscribed in the design science research paradigm, this study presents a novel Python package that combines chord diagrams with natural language processing (NLP) techniques to obtain insights from online reviews{\textquoteright} textual content. Preprocessing and sentiment analysis transform unstructured text into structured data, revealing prevalent themes and co-occurring associations. The Chord Reviews package{\textquoteright}s resultant artifact demonstrates robust functionality, completeness, consistency, accuracy, performance, reliability, and usability across diverse domains and datasets. This package offers businesses a versatile tool for textual analysis, enabling informed decision-making to enhance customer satisfaction, product development, or market competitiveness.",

keywords = "Data visualization, Text analytics, Customer reviews, Design science research, Chord diagrams",

author = "F{\'e}lix Funes and Nuno Ant{\'o}nio",

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year = "2026",

month = may,

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doi = "10.1007/978-3-032-20132-4\_5",

language = "English",

isbn = "978-3-032-20131-7",

series = "Developments in Marketing Science: Proceedings of the Academy of Marketing Science",

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pages = "43--55",

editor = "Jasmine Parajuli and Amrita Dey",

booktitle = "Going Back to the Roots of Marketing",

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Funes, F & António, N 2026, Chord Reviews: A Python Package for Analyzing Online Reviews. in J Parajuli & A Dey (eds), Going Back to the Roots of Marketing: Proceedings of the 2025 AMS Annual Conference, Montreal, Quebec, Canada, May 21-23. Developments in Marketing Science: Proceedings of the Academy of Marketing Science, Springer, Cham, Switzerland, pp. 43-55, 2025 Academy of Marketing Science World Marketing Congress, Dijon, France, 1/07/25. https://doi.org/10.1007/978-3-032-20132-4_5

Chord Reviews: A Python Package for Analyzing Online Reviews. / Funes, Félix ; António, Nuno.
Going Back to the Roots of Marketing: Proceedings of the 2025 AMS Annual Conference, Montreal, Quebec, Canada, May 21-23. ed. / Jasmine Parajuli; Amrita Dey. Cham, Switzerland: Springer, 2026. p. 43-55 (Developments in Marketing Science: Proceedings of the Academy of Marketing Science).

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

TY - GEN

T1 - Chord Reviews

T2 - 2025 Academy of Marketing Science World Marketing Congress

AU - Funes, Félix

AU - António, Nuno

N1 - Conference code: 26

PY - 2026/5/1

Y1 - 2026/5/1

N2 - User Generated Content (UGC), particularly online customer reviews, serves as a crucial source of feedback for businesses by offering insights into customer sentiments and preferences. However, these reviews’ unstructured nature and sheer volume present significant challenges in extracting actionable insights. This study addresses this challenge by proposing an innovative approach that combines data visualization and text mining techniques to optimize the discovery of insights from online reviews. Inscribed in the design science research paradigm, this study presents a novel Python package that combines chord diagrams with natural language processing (NLP) techniques to obtain insights from online reviews’ textual content. Preprocessing and sentiment analysis transform unstructured text into structured data, revealing prevalent themes and co-occurring associations. The Chord Reviews package’s resultant artifact demonstrates robust functionality, completeness, consistency, accuracy, performance, reliability, and usability across diverse domains and datasets. This package offers businesses a versatile tool for textual analysis, enabling informed decision-making to enhance customer satisfaction, product development, or market competitiveness.

AB - User Generated Content (UGC), particularly online customer reviews, serves as a crucial source of feedback for businesses by offering insights into customer sentiments and preferences. However, these reviews’ unstructured nature and sheer volume present significant challenges in extracting actionable insights. This study addresses this challenge by proposing an innovative approach that combines data visualization and text mining techniques to optimize the discovery of insights from online reviews. Inscribed in the design science research paradigm, this study presents a novel Python package that combines chord diagrams with natural language processing (NLP) techniques to obtain insights from online reviews’ textual content. Preprocessing and sentiment analysis transform unstructured text into structured data, revealing prevalent themes and co-occurring associations. The Chord Reviews package’s resultant artifact demonstrates robust functionality, completeness, consistency, accuracy, performance, reliability, and usability across diverse domains and datasets. This package offers businesses a versatile tool for textual analysis, enabling informed decision-making to enhance customer satisfaction, product development, or market competitiveness.

KW - Data visualization

KW - Text analytics

KW - Customer reviews

KW - Design science research

KW - Chord diagrams

UR - https://www.scopus.com/pages/publications/105046729281

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001826948400005

U2 - 10.1007/978-3-032-20132-4_5

DO - 10.1007/978-3-032-20132-4_5

M3 - Conference contribution

SN - 978-3-032-20131-7

SN - 978-3-032-20134-8

T3 - Developments in Marketing Science: Proceedings of the Academy of Marketing Science

SP - 43

EP - 55

BT - Going Back to the Roots of Marketing

A2 - Parajuli, Jasmine

A2 - Dey, Amrita

PB - Springer

CY - Cham, Switzerland

Y2 - 1 July 2025 through 4 July 2025

ER -

Funes F , António N. Chord Reviews: A Python Package for Analyzing Online Reviews. In Parajuli J, Dey A, editors, Going Back to the Roots of Marketing: Proceedings of the 2025 AMS Annual Conference, Montreal, Quebec, Canada, May 21-23. Cham, Switzerland: Springer. 2026. p. 43-55. (Developments in Marketing Science: Proceedings of the Academy of Marketing Science). doi: 10.1007/978-3-032-20132-4_5

Chord Reviews: A Python Package for Analyzing Online Reviews

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Keywords

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