Alignment and Inconsistency between Textual Sentiment and Star Ratings in Online Product Reviews: A Reflexive Thematic Analysis
DOI:
https://doi.org/10.33394/jollt.v14i3.19999Keywords:
Online reviews, Politeness, Sentiment-rating alignment, Reflexive thematic analysis, Evaluative languageAbstract
Online product reviews combine numerical star ratings with written reviews to communicate consumer evaluations. While recent studies highlight frequent mismatches between textual sentiment and numerical ratings, few have examined this phenomenon qualitatively. This research investigates how linguistic sentiment aligns or diverges from star ratings to understand the pragmatic functions behind these inconsistencies. The research analyzes 120 anonymized furniture reviews collected from the IKEA Australia website. A two-stage methodology was applied: a descriptive overview classifying sentiment-rating consistency, followed by Reflexive Thematic Analysis (RTA) to examine linguistic richness and rating alignment. The analysis reveals four dominant evaluative patterns: Polite Dissatisfaction, Understated Positivity, Expressive Dissatisfaction, and Low-Information Reviews. Reviewers frequently employ politeness strategies, contrastive structures, and evaluative restraint, resulting in partial or complete mismatches between expressed sentiment and numerical scores. These results indicate that star ratings and written texts serve distinct communicative purposes and should be interpreted together rather than in isolation. The research contributes to qualitative sentiment analysis and digital pragmatics by demonstrating that sentiment-rating inconsistency is a deliberate pragmatic strategy rather than an analytical measurement error.
References
Abierto, A., & Fortanet-Gómez, I. (2022). Applying appraisal theory for the interpretation of experienced researchers’ interviews on open access. Revista Signos, 55(109), 481–500. https://doi.org/10.4067/S0718-09342022000200481
Atteveldt, V., & Velden, V. D. (2022). The validity of sentiment analysis: Comparing manual annotation, crowd-coding, dictionary approaches, and machine learning algorithms. https://doi.org/10.1080/19312458.2020.1869198
Baker, M. J., & Hashimoto, B. (2024). Expression of customer (dis)satisfaction in online restaurant reviews: The relationship between adversative connective constructions and star ratings. https://doi.org/10.1177/23294884231200245
Braun, V., & Clarke, V. (2023). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://psychology.ukzn.ac.za/?mdocs-file=1176
Brown, P., & Levinson, S. C. (2006). Politeness theory. In A. Jaworski & N. Coupland (Eds.), The discourse reader (2nd ed., p. 547). Routledge.
Chang, J. P., Cheng, J., & Danescu-Niculescu-Mizil, C. (2020). Don’t let me be misunderstood: Comparing intentions and perceptions in online discussions. Proceedings of The Web Conference 2020, 2066–2077. https://doi.org/10.48550/arXiv.2004.13609
Dong, L., Ji, T., & Zhang, J. (2022). Effects of conversation politeness on hiring decision in online labor markets: An inverted U-shaped relationship exploration.
Gao, H. (2023). The impact of topological structure, product category, and online reviews on co-purchase: A network perspective, 548–570.
Gupta, N., Di Fabbrizio, G., & Haffner, P. (2010). Capturing the stars: Predicting ratings for service and product reviews, 36–43.
Kashiha, H. (2023). Beyond words in evaluation: A study of research articles across disciplines. Journal of English for Academic Purposes, 27(2), 251–275.
Khedkar, B. (2018). Sentiment analysis of customer product reviews classification techniques: A review. International Journal of Computer Sciences and Engineering, 5(11), 148–150.
Liu, Z., Courant, R., & Kalogeiton, V. (2022). FunnyNet: Audiovisual learning of funny moments in videos. HAL. https://hal.science/hal-03839553
Liu, Z., & Mahmud, J. (2021). When and why does a model fail? A human-in-the-loop error detection framework for sentiment analysis, 170–177.
Lopez, A., & Garza, R. (2022). Do sensory reviews make more sense? The mediation of objective perception in online review helpfulness. https://doi.org/10.1108/JRIM-04-2021-0121
Meuleman, B., Moors, A., Fontaine, J. R. J., & Renaud, O. (2019). Interaction and threshold effects of appraisal on componential patterns of emotion: A study using cross-cultural semantic data. https://doi.org/10.1037/EMO0000449
Park, Y. J., Joo, J., Polpanumas, C., & Yoon, Y. (2021). “Worse than what I read?” The external effect of review ratings on the online review generation process: An empirical analysis of multiple product categories using Amazon.com review data.
Patton, M. Q. (2002). Qualitative research and evaluation methods (3rd ed.). SAGE Publications.
Qin, C., & Zeng, X. (2023). Do live streaming and online consumer reviews jointly affect purchase intention?
Raghunathan, N., & Kandasamy, S. (2023). Challenges and issues in sentiment analysis: A comprehensive survey. IEEE Access, 11, 1. https://doi.org/10.1109/ACCESS.2023.3293041
Ruytenbeek, N., & Decock, S. (2024). Expressing and responding to customer (dis)satisfaction online: New insights from discourse and linguistic approaches. https://doi.org/10.1177/23294884231199740
Sachdeva, N., & McAuley, J. (2026). How useful are reviews for recommendation? A critical review and potential improvements. https://doi.org/10.1145/3397271.3401281
Schneider, C., Weinmann, M., Mohr, P. N. C., & Brocke, J. (2026). When the stars shine too bright: The influence of multidimensional ratings on online consumer ratings.
Sharma, S. (2023). Boosting sales and building trust: Leveraging product reviews for marketing success. In Managing product reviews: A comprehensive guide for brands and businesses (pp. 115–134).
Thomas, M., & Weyerer, J. C. (2019). Determinants of online review credibility and its impact on consumers’ purchase. Journal of Electronic Commerce Research, 20(1), 1–20.
Yoon, E. J. (2020). Polite speech emerges from competing social goals.
Ziser, Y., Webber, B., & Cohen, S. B. (2023). Rant or rave: Variation over time in the language of online reviews. Language Resources and Evaluation, 57(3). https://doi.org/10.1007/s10579-023-09652-5
Downloads
Published
How to Cite
Issue
Section
Citation Check
License
Copyright (c) 2026 Sastika Seli, Yulfi Yulfi, Episiasi Episiasi

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
License and Publishing Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and publishing agreement.
Copyright
Authors who publish with JOLLT Journal of Languages and Language Teaching agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Licensing for Data Publication
-
Open Data Commons Attribution License, http://www.opendatacommons.org/licenses/by/1.0/ (default)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.














