The University of Southampton
University of Southampton Institutional Repository

Identifying nostalgia in text: the development and validation of the Nostalgia Dictionary

Identifying nostalgia in text: the development and validation of the Nostalgia Dictionary
Identifying nostalgia in text: the development and validation of the Nostalgia Dictionary
Nostalgia is a prevalent emotion that confers psychological benefits and influences consumer behavior. We developed and validated the 98-word Nostalgia Dictionary to automatize the assessment of nostalgicity in narratives (e.g., customer reviews, social media). First, we created an initial wordlist by identifying the most frequently used words in nostalgia narratives and by relying on the nostalgia literature. Second, we finalized the dictionary by testing experimentally the expanded wordlist for its capacity to differentiate nostalgia from related emotions. Third, we validated the dictionary by demonstrating that it corresponds to self-reports and coder-ratings of nostalgia, produces result patterns expected by theory, and predicts favorability ratings of books and consumer experiences, even after adjusting for positive emotion words. We discuss the potential of the Nostalgia Dictionary to advance research and practice.
consumer behavior, consumer experiences, nostalgia, nostalgia dictionary, text mining
1057-7408
728-742
Chen, Jia
3b32661d-16b8-46ed-9fee-8cbacd390119
Layous, Kristin
ad480cc6-aa19-4a1f-9461-262c1348b5b4
Wildschut, Tim
4452a61d-1649-4c4a-bb1d-154ec446ff81
Sedikides, Constantine
9d45e66d-75bb-44de-87d7-21fd553812c2
Chen, Jia
3b32661d-16b8-46ed-9fee-8cbacd390119
Layous, Kristin
ad480cc6-aa19-4a1f-9461-262c1348b5b4
Wildschut, Tim
4452a61d-1649-4c4a-bb1d-154ec446ff81
Sedikides, Constantine
9d45e66d-75bb-44de-87d7-21fd553812c2

Chen, Jia, Layous, Kristin, Wildschut, Tim and Sedikides, Constantine (2023) Identifying nostalgia in text: the development and validation of the Nostalgia Dictionary. Journal of Consumer Psychology, 33 (4), 728-742. (doi:10.1002/jcpy.1359).

Record type: Article

Abstract

Nostalgia is a prevalent emotion that confers psychological benefits and influences consumer behavior. We developed and validated the 98-word Nostalgia Dictionary to automatize the assessment of nostalgicity in narratives (e.g., customer reviews, social media). First, we created an initial wordlist by identifying the most frequently used words in nostalgia narratives and by relying on the nostalgia literature. Second, we finalized the dictionary by testing experimentally the expanded wordlist for its capacity to differentiate nostalgia from related emotions. Third, we validated the dictionary by demonstrating that it corresponds to self-reports and coder-ratings of nostalgia, produces result patterns expected by theory, and predicts favorability ratings of books and consumer experiences, even after adjusting for positive emotion words. We discuss the potential of the Nostalgia Dictionary to advance research and practice.

Text
Chen et al., in press, Journal of Consumer Psychology - Accepted Manuscript
Restricted to Repository staff only until 27 April 2025.
Request a copy

More information

Accepted/In Press date: 27 April 2023
e-pub ahead of print date: 10 May 2023
Published date: October 2023
Additional Information: Funding Information: This work was sponsored by Shanghai Pujiang Program [grant number 22PJC077] and ShanghaiTech Start‐up Fund. Publisher Copyright: © 2023 Society for Consumer Psychology.
Keywords: consumer behavior, consumer experiences, nostalgia, nostalgia dictionary, text mining

Identifiers

Local EPrints ID: 476943
URI: http://eprints.soton.ac.uk/id/eprint/476943
ISSN: 1057-7408
PURE UUID: 045ce884-9acb-4bd7-8bf9-26b74ed096bc
ORCID for Tim Wildschut: ORCID iD orcid.org/0000-0002-6499-5487
ORCID for Constantine Sedikides: ORCID iD orcid.org/0000-0003-4036-889X

Catalogue record

Date deposited: 22 May 2023 16:31
Last modified: 17 Mar 2024 02:53

Export record

Altmetrics

Contributors

Author: Jia Chen
Author: Kristin Layous
Author: Tim Wildschut ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×