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How to win the green market? Exploring the satisfaction and sentiment of Chinese consumers based on text mining.

Authors :
Li, Chuang
Niu, Yating
Wang, Liping
Source :
Computers in Human Behavior. Nov2023, Vol. 148, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

In order to fully understand the overall evaluations and sentiments of green products by the Chinese public, and comprehensively enhance the public's awareness of green consumption, this paper conducts sentiment analysis and text mining based on 85306 online reviews of 8 typical green products in four major fields of China's JingDong (JD.com) e-commerce platform, including organic food, daily cleaning, green building materials, and energy-saving home appliances. Research has found that: (1) The consumers' preferences and attentions to different attributes of green products vary depending on the product; (2) The public's overall evaluations and satisfactions with existing products are relatively high; (3) Found 9 topics related to consumers' positive sentiments (i.e., enthusiastic service, beautiful appearance, good taste, good quality, favorable price, trustworthy brand, powerful function and effect, fast logistics and delivery, high-cost performance) and 3 topics related to negative sentiments (i.e., poor service attitude, smelly, and noise); (4) Consumers' attentions, positive and negative sentiments for most of the attributes of this green product show an upward trend to varying degrees. The results of this paper provide reliable practical evidence for the promotion and use of green products, which is of great significance for promoting green consumption. • The paper established an online reviews analysis framework for green production. • We explored consumers' overall evaluations and sentiments towards green products. • We have found 9 topics to consumers' positive sentiments and 3 topics to negative. • Consumers' preferences for green attributes vary depending on the products. • Helping to enrich the sources and acquisition methods of green consumption data. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
07475632
Volume :
148
Database :
Academic Search Index
Journal :
Computers in Human Behavior
Publication Type :
Academic Journal
Accession number :
171313706
Full Text :
https://doi.org/10.1016/j.chb.2023.107890