Abstract
Online reviews are essential to consumers' decision-making when purchasing products on e-commerce platforms. Most of the existing research conducts sentiment analysis on online reviews, yet the disclosure characteristics of these reviews have not received sufficient attention. Therefore, this paper evaluated the information characteristics of online reviews using review length, readability, redundancy, and specificity indicators. By collecting 18,131 online clothing reviews, we applied Latent Dirichlet allocation to divide the review texts into nine topics. We also investigate the relationship between review text characteristics and review sentiment and verify the robustness of the results using different regression models. We conclude that a review with more words, higher redundancy, lower fog index, and lower specificity tends to express a more positive emotion of the review. Our research will help e-commerce platforms construct general review writing guidelines to improve consumer satisfaction.
Similar content being viewed by others
References
Hong, W., Yu, Z., Wu, L., & Pu, X. (2020). Influencing factors of the persuasiveness of online reviews considering persuasion methods. Electronic Commerce Research and Applications. https://doi.org/10.1016/j.elerap.2019.100912
Shihab, M. R., & Putri, A. P. (2018). Negative online reviews of popular products: Understanding the effects of review proportion and quality on consumers’ attitude and intention to buy. Electronic Commerce Research, 19(1), 159–187. https://doi.org/10.1007/s10660-018-9294-y
Fresneda, J. E., & Gefen, D. (2019). A semantic measure of online review helpfulness and the importance of message entropy. Decision Support Systems, 125, 113117. https://doi.org/10.1016/j.dss.2019.113117
Biswas, B., Sengupta, P., Kumar, A., Delen, D., & Gupta, S. (2022). A critical assessment of consumer reviews: A hybrid NLP-based methodology. Decision Support Systems, 159, 113799. https://doi.org/10.1016/j.dss.2022.113799
Gregoriades, A., & Pampaka, M. (2020). Electronic word of mouth analysis for new product positioning evaluation. Electronic Commerce Research and Applications. https://doi.org/10.1016/j.elerap.2020.100986
Liu, Y., Gan, W., & Zhang, Q. (2021). Decision-making mechanism of online retailer based on additional online comments of consumers. Journal of Retailing and Consumer Services. https://doi.org/10.1016/j.jretconser.2020.102389
Bi, S., Liu, Z., & Usman, K. (2017). The influence of online information on investing decisions of reward-based crowdfunding. Journal of Business Research, 71, 10–18. https://doi.org/10.1016/j.jbusres.2016.10.001
Eitel, A., Scheiter, K., Schüler, A., Nyström, M., & Holmqvist, K. (2013). How a picture facilitates the process of learning from text: Evidence for scaffolding. Learning and Instruction, 28, 48–63. https://doi.org/10.1016/j.learninstruc.2013.05.002
Korfiatis, N., Stamolampros, P., Kourouthanassis, P., & Sagiadinos, V. (2019). Measuring service quality from unstructured data: A topic modeling application on airline passengers’ online reviews. Expert Systems with Applications, 116, 472–486. https://doi.org/10.1016/j.eswa.2018.09.037
Zhan, J., Loh, H. T., & Liu, Y. (2009). Gather customer concerns from online product reviews—a text summarization approach. Expert Systems with Applications, 36(2), 2107–2115. https://doi.org/10.1016/j.eswa.2007.12.039
Shah, A. M., Yan, X., Qayyum, A., Naqvi, R. A., & Shah, S. J. (2021). Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach. International Journal of Medical Informatics, 149, 104434. https://doi.org/10.1016/j.ijmedinf.2021.104434
Li, F. (2008). Annual report readability, current earnings, and earnings persistence. Journal of Accounting and Economics, 45(2–3), 221–247. https://doi.org/10.1016/j.jacceco.2008.02.003
You, H., & Zhang, X. (2009). Financial reporting complexity and investor underreaction to 10-K information. Review of Accounting Studies, 14(4), 559–586. https://doi.org/10.1007/s11142-008-9083-2
Loughran, T. I. M., & McDonald, B. (2014). Measuring readability in financial disclosures. The Journal of Finance, 69(4), 1643–1671. https://doi.org/10.1111/jofi.12162
Chen, M.-Y., & Teng, C.-I. (2013). A comprehensive model of the effects of online store image on purchase intention in an e-commerce environment. Electronic Commerce Research, 13(1), 1–23. https://doi.org/10.1007/s10660-013-9104-5
Beuckels, E., & Hudders, L. (2016). An experimental study to investigate the impact of image interactivity on the perception of luxury in an online shopping context. Journal of Retailing and Consumer Services, 33, 135–142. https://doi.org/10.1016/j.jretconser.2016.08.014
Wang, S., Lin, Y., & Zhu, G. (2023). Online reviews and high-involvement product sales: Evidence from offline sales in the chinese automobile industry. Electronic Commerce Research and Applications, 57, 101231. https://doi.org/10.1016/j.elerap.2022.101231
Park, D.-H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125–148. https://doi.org/10.2753/JEC1086-4415110405
Xia, H., Yang, Y., Pan, X., Zhang, Z., & An, W. (2020). Sentiment analysis for online reviews using conditional random fields and support vector machines. Electronic Commerce Research, 20(2), 343–360. https://doi.org/10.1007/s10660-019-09354-7
Jain, P. K., Pamula, R., & Srivastava, G. (2021). A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews. Computer Science Review, 41, 100413. https://doi.org/10.1016/j.cosrev.2021.100413
Singh, J., Singh, G., & Singh, R. (2017). Optimization of sentiment analysis using machine learning classifiers. Human-centric Computing and Information Sciences, 7(1), 32. https://doi.org/10.1186/s13673-017-0116-3
Fan, Z.-P., Che, Y.-J., & Chen, Z.-Y. (2017). Product sales forecasting using online reviews and historical sales data: A method combining the bass model and sentiment analysis. Journal of Business Research, 74, 90–100. https://doi.org/10.1016/j.jbusres.2017.01.010
Li, X., Wu, C., & Mai, F. (2019). The effect of online reviews on product sales: A joint sentiment-topic analysis. Information and Management, 56(2), 172–184. https://doi.org/10.1016/j.im.2018.04.007
Muhammad, A., Wiratunga, N., & Lothian, R. (2016). Contextual sentiment analysis for social media genres. Knowledge-Based Systems, 108, 92–101. https://doi.org/10.1016/j.knosys.2016.05.032
Khan, F. H., Qamar, U., & Bashir, S. (2017). A semi-supervised approach to sentiment analysis using revised sentiment strength based on SentiWordNet. Knowledge and Information Systems, 51(3), 851–872. https://doi.org/10.1007/s10115-016-0993-1
Saif, H., He, Y., Fernandez, M., & Alani, H. (2016). Contextual semantics for sentiment analysis of Twitter. Information Processing and Management, 52(1), 5–19. https://doi.org/10.1016/j.ipm.2015.01.005
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics, 37(2), 267–307. https://doi.org/10.1162/COLI_a_00049
Hogenboom, A., Heerschop, B., Frasincar, F., Kaymak, U., & de Jong, F. (2014). Multi-lingual support for lexicon-based sentiment analysis guided by semantics. Decision Support Systems, 62, 43–53. https://doi.org/10.1016/j.dss.2014.03.004
Xianghua, F., Guo, L., Yanyan, G., & Zhiqiang, W. (2013). Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowledge-Based Systems, 37, 186–195. https://doi.org/10.1016/j.knosys.2012.08.003
Liu, L., Lei, M., & Wang, H. (2013). Combining domain-specific sentiment Lexicon with Hownet for Chinese sentiment analysis. Journal of Computers, 8(4), 878–883. https://doi.org/10.4304/jcp.8.4.878-883
Peng, H., Cambria, E., & Hussain, A. (2017). A review of sentiment analysis research in Chinese language. Cognitive Computation, 9(4), 423–435. https://doi.org/10.1007/s12559-017-9470-8
Gerdes, J., Stringam, B. B., & Brookshire, R. G. (2008). An integrative approach to assess qualitative and quantitative consumer feedback. Electronic Commerce Research, 8(4), 217–234. https://doi.org/10.1007/s10660-008-9022-0
Korfiatis, N., García-Bariocanal, E., & Sánchez-Alonso, S. (2012). Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electronic Commerce Research and Applications, 11(3), 205–217. https://doi.org/10.1016/j.elerap.2011.10.003
Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140–151. https://doi.org/10.1016/j.tourman.2014.09.020
Ghose, A., Ipeirotis, P. G., & Li, B. (2012). Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, 31(3), 493–520. https://doi.org/10.1287/mksc.1110.0700
Burtch, G., Ghose, A., & Wattal, S. (2013). An empirical examination of the antecedents and consequences of contribution patterns in crowd-funded markets. Information Systems Research, 24(3), 499–519. https://doi.org/10.1287/isre.1120.0468
Connors, L., Mudambi, S. M., & Schuff, D. (2011). Is it the review or the reviewer? A multi-method approach to determine the antecedents of online review helpfulness. In 2011 44th Hawaii International Conference on System Sciences (pp. 1–10). Presented at the 2011 44th Hawaii International Conference on System Sciences. https://doi.org/10.1109/HICSS.2011.260
Dyer, T., Lang, M., & Stice-Lawrence, L. (2017). The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation. Journal of Accounting and Economics, 64(2–3), 221–245. https://doi.org/10.1016/j.jacceco.2017.07.002
Hope, O.-K., Hu, D., & Lu, H. (2016). The benefits of specific risk-factor disclosures. Review of Accounting Studies, 21(4), 1005–1045. https://doi.org/10.1007/s11142-016-9371-1
He, S., & Wang, Y. (2023). Evaluating new energy vehicles by picture fuzzy sets based on sentiment analysis from online reviews. Artificial Intelligence Review, 56(3), 2171–2192. https://doi.org/10.1007/s10462-022-10217-1
Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product features by mining consumer reviews. Management Science, 57(8), 1485–1509. https://doi.org/10.1287/mnsc.1110.1370
Mou, J., Ren, G., Qin, C., & Kurcz, K. (2019). Understanding the topics of export cross-border e-commerce consumers feedback: An LDA approach. Electronic Commerce Research, 19(4), 749–777. https://doi.org/10.1007/s10660-019-09338-7
Wang, W., Feng, Y., & Dai, W. (2018). Topic analysis of online reviews for two competitive products using latent Dirichlet allocation. Electronic Commerce Research and Applications, 29, 142–156. https://doi.org/10.1016/j.elerap.2018.04.003
Pavlinek, M., & Podgorelec, V. (2017). Text classification method based on self-training and LDA topic models. Expert Systems with Applications, 80, 83–93. https://doi.org/10.1016/j.eswa.2017.03.020
Bao, Y., & Datta, A. (2014). Simultaneously discovering and quantifying risk types from textual risk disclosures. Management Science, 60(6), 1371–1391. https://doi.org/10.1287/mnsc.2014.1930
Hu, Y., Zhou, H., Chen, Y., Yao, J., & Su, J. (2021). The influence of patient-generated reviews and doctor-patient relationship on online consultations in China. Electronic Commerce Research. https://doi.org/10.1007/s10660-021-09506-8
Zhang, J. Q., Craciun, G., & Shin, D. (2010). When does electronic word-of-mouth matter? A study of consumer product reviews. Journal of Business Research, 63(12), 1336–1341. https://doi.org/10.1016/j.jbusres.2009.12.011
Maslowska, E., Malthouse, E. C., & Bernritter, S. F. (2017). Too good to be true: The role of online reviews’ features in probability to buy. International Journal of Advertising, 36(1), 142–163. https://doi.org/10.1080/02650487.2016.1195622
Ajina, A., Laouiti, M., & Msolli, B. (2016). Guiding through the fog: Does annual report readability reveal earnings management? Research in International Business and Finance, 38, 509–516. https://doi.org/10.1016/j.ribaf.2016.07.021
Biddle, G. C., Hilary, G., & Verdi, R. S. (2009). How does financial reporting quality relate to investment efficiency? Journal of Accounting and Economics, 48(2–3), 112–131. https://doi.org/10.1016/j.jacceco.2009.09.001
Lawrence, A. (2013). Individual investors and financial disclosure. Journal of Accounting and Economics, 56(1), 130–147. https://doi.org/10.1016/j.jacceco.2013.05.001
Harjoto, M. A., Laksmana, I., & Lee, W. E. (2020). Female leadership in corporate social responsibility reporting: effects on writing, readability and future social performance. Advances in Accounting. https://doi.org/10.1016/j.adiac.2020.100475
Bhardwaj, A., & Imam, S. (2019). The tone and readability of the media during the financial crisis: Evidence from pre-IPO media coverage. International Review of Financial Analysis, 63, 40–48. https://doi.org/10.1016/j.irfa.2019.02.001
Choi, H. S., & Leon, S. (2020). An empirical investigation of online review helpfulness: A big data perspective. Decision Support Systems, 139, 113403. https://doi.org/10.1016/j.dss.2020.113403
Acknowledgements
This work was supported by grants from the National Natural Science Foundation of China (72001223, 72371258), the Program for "Elite Scholars" in Central University of Finance and Economics, and the Program for Innovation Research in Central University of Finance and Economics
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix A
Appendix A
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wei, L., Ma, S. & Wang, M. Understanding the information characteristics of consumers’ online reviews: the evidence from Chinese online apparel shopping. Electron Commer Res (2023). https://doi.org/10.1007/s10660-023-09784-4
Accepted:
Published:
DOI: https://doi.org/10.1007/s10660-023-09784-4