Beyond the Scent: A Holistic NLP Study of the Fragrance World

Simay Turan; Fatih Kerem Doğan; Tolga Kaya

doi:10.1007/978-3-031-67192-0_2

Beyond the Scent: A Holistic NLP Study of the Fragrance World

Simay Turan^*, Fatih Kerem Doğan, Tolga Kaya

^*Corresponding author for this work

Department of Management Engineering

Istanbul Technical University

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

Abstract

This paper presents a holistic NLP-based approach to the fragrance industry. Using a methodology based on online reviews, this study aimed to gain insights into customer sentiment and product preferences towards fragrance brands. In this study, analyses were conducted by creating a unique dataset by randomly selecting 13,828 reviews of 36 perfumes from a website specializing in the global perfume industry. By sentiment analysis, it is investigated how well user comments’ sentiment compound scores and overall perfume evaluations correlate. Using Latent Dirichlet Allocation method, commonly used terms and topics are extracted from comments to provide insights into user sentiments. Moreover, Term Frequency-Inverse Document Frequency analysis helped to draw out keywords and word patterns from evaluations that are distinctive to a given brand. The most commonly used words in these comments were identified by examining user reviews. By bridging perfumes and language within user-generated content, we believe this study would contribute valuable insights to management practices in the fragrance industry.

Original language	English
Title of host publication	Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Selcuk Cebi, Basar Oztaysi, Irem Ucal Sari, A. Cagrı Tolga
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	11-19
Number of pages	9
ISBN (Print)	9783031671913
DOIs	https://doi.org/10.1007/978-3-031-67192-0_2
Publication status	Published - 2024
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2024 - Canakkale, Turkey Duration: 16 Jul 2024 → 18 Jul 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1090 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2024
Country/Territory	Turkey
City	Canakkale
Period	16/07/24 → 18/07/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Fragrance
Latent Dirichlet Allocation
Machine Learning
Natural Language Processing
Perfume
Sentiment Analysis
Topic Modelling

Access to Document

10.1007/978-3-031-67192-0_2

Cite this

Turan, S., Doğan, F. K., & Kaya, T. (2024). Beyond the Scent: A Holistic NLP Study of the Fragrance World. In C. Kahraman, S. Cevik Onar, S. Cebi, B. Oztaysi, I. Ucal Sari, & A. C. Tolga (Eds.), Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference (pp. 11-19). (Lecture Notes in Networks and Systems; Vol. 1090 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-67192-0_2

Turan, Simay ; Doğan, Fatih Kerem ; Kaya, Tolga. / Beyond the Scent : A Holistic NLP Study of the Fragrance World. Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Selcuk Cebi ; Basar Oztaysi ; Irem Ucal Sari ; A. Cagrı Tolga. Springer Science and Business Media Deutschland GmbH, 2024. pp. 11-19 (Lecture Notes in Networks and Systems).

@inproceedings{76e763990cc7435eaffafe72b6e139c2,

title = "Beyond the Scent: A Holistic NLP Study of the Fragrance World",

abstract = "This paper presents a holistic NLP-based approach to the fragrance industry. Using a methodology based on online reviews, this study aimed to gain insights into customer sentiment and product preferences towards fragrance brands. In this study, analyses were conducted by creating a unique dataset by randomly selecting 13,828 reviews of 36 perfumes from a website specializing in the global perfume industry. By sentiment analysis, it is investigated how well user comments{\textquoteright} sentiment compound scores and overall perfume evaluations correlate. Using Latent Dirichlet Allocation method, commonly used terms and topics are extracted from comments to provide insights into user sentiments. Moreover, Term Frequency-Inverse Document Frequency analysis helped to draw out keywords and word patterns from evaluations that are distinctive to a given brand. The most commonly used words in these comments were identified by examining user reviews. By bridging perfumes and language within user-generated content, we believe this study would contribute valuable insights to management practices in the fragrance industry.",

keywords = "Fragrance, Latent Dirichlet Allocation, Machine Learning, Natural Language Processing, Perfume, Sentiment Analysis, Topic Modelling",

author = "Simay Turan and Doğan, {Fatih Kerem} and Tolga Kaya",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; International Conference on Intelligent and Fuzzy Systems, INFUS 2024 ; Conference date: 16-07-2024 Through 18-07-2024",

year = "2024",

doi = "10.1007/978-3-031-67192-0_2",

language = "English",

isbn = "9783031671913",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "11--19",

editor = "Cengiz Kahraman and {Cevik Onar}, Sezi and Selcuk Cebi and Basar Oztaysi and {Ucal Sari}, Irem and Tolga, {A. Cagrı}",

booktitle = "Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference",

address = "Germany",

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Turan, S, Doğan, FK & Kaya, T 2024, Beyond the Scent: A Holistic NLP Study of the Fragrance World. in C Kahraman, S Cevik Onar, S Cebi, B Oztaysi, I Ucal Sari & AC Tolga (eds), Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. Lecture Notes in Networks and Systems, vol. 1090 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 11-19, International Conference on Intelligent and Fuzzy Systems, INFUS 2024, Canakkale, Turkey, 16/07/24. https://doi.org/10.1007/978-3-031-67192-0_2

Beyond the Scent: A Holistic NLP Study of the Fragrance World. / Turan, Simay; Doğan, Fatih Kerem; Kaya, Tolga.
Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Selcuk Cebi; Basar Oztaysi; Irem Ucal Sari; A. Cagrı Tolga. Springer Science and Business Media Deutschland GmbH, 2024. p. 11-19 (Lecture Notes in Networks and Systems; Vol. 1090 LNNS).

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

TY - GEN

T1 - Beyond the Scent

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2024

AU - Turan, Simay

AU - Doğan, Fatih Kerem

AU - Kaya, Tolga

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - This paper presents a holistic NLP-based approach to the fragrance industry. Using a methodology based on online reviews, this study aimed to gain insights into customer sentiment and product preferences towards fragrance brands. In this study, analyses were conducted by creating a unique dataset by randomly selecting 13,828 reviews of 36 perfumes from a website specializing in the global perfume industry. By sentiment analysis, it is investigated how well user comments’ sentiment compound scores and overall perfume evaluations correlate. Using Latent Dirichlet Allocation method, commonly used terms and topics are extracted from comments to provide insights into user sentiments. Moreover, Term Frequency-Inverse Document Frequency analysis helped to draw out keywords and word patterns from evaluations that are distinctive to a given brand. The most commonly used words in these comments were identified by examining user reviews. By bridging perfumes and language within user-generated content, we believe this study would contribute valuable insights to management practices in the fragrance industry.

AB - This paper presents a holistic NLP-based approach to the fragrance industry. Using a methodology based on online reviews, this study aimed to gain insights into customer sentiment and product preferences towards fragrance brands. In this study, analyses were conducted by creating a unique dataset by randomly selecting 13,828 reviews of 36 perfumes from a website specializing in the global perfume industry. By sentiment analysis, it is investigated how well user comments’ sentiment compound scores and overall perfume evaluations correlate. Using Latent Dirichlet Allocation method, commonly used terms and topics are extracted from comments to provide insights into user sentiments. Moreover, Term Frequency-Inverse Document Frequency analysis helped to draw out keywords and word patterns from evaluations that are distinctive to a given brand. The most commonly used words in these comments were identified by examining user reviews. By bridging perfumes and language within user-generated content, we believe this study would contribute valuable insights to management practices in the fragrance industry.

KW - Fragrance

KW - Latent Dirichlet Allocation

KW - Machine Learning

KW - Natural Language Processing

KW - Perfume

KW - Sentiment Analysis

KW - Topic Modelling

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U2 - 10.1007/978-3-031-67192-0_2

DO - 10.1007/978-3-031-67192-0_2

M3 - Conference contribution

AN - SCOPUS:85203194260

SN - 9783031671913

T3 - Lecture Notes in Networks and Systems

SP - 11

EP - 19

BT - Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Cebi, Selcuk

A2 - Oztaysi, Basar

A2 - Ucal Sari, Irem

A2 - Tolga, A. Cagrı

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 16 July 2024 through 18 July 2024

ER -

Turan S, Doğan FK, Kaya T. Beyond the Scent: A Holistic NLP Study of the Fragrance World. In Kahraman C, Cevik Onar S, Cebi S, Oztaysi B, Ucal Sari I, Tolga AC, editors, Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. Springer Science and Business Media Deutschland GmbH. 2024. p. 11-19. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-67192-0_2

Beyond the Scent: A Holistic NLP Study of the Fragrance World

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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