Quantifying Post-Purchase Service Satisfaction: A Topic–Emotion Fusion Approach with Smartphone Data
I tiakina i:
| I whakaputaina i: | Big Data and Cognitive Computing vol. 9, no. 5 (2025), p. 125 |
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| Kaituhi matua: | |
| Ētahi atu kaituhi: | , |
| I whakaputaina: |
MDPI AG
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| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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MARC
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|---|---|---|---|
| 001 | 3211858310 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-2289 | ||
| 024 | 7 | |a 10.3390/bdcc9050125 |2 doi | |
| 035 | |a 3211858310 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Guo Peijun | |
| 245 | 1 | |a Quantifying Post-Purchase Service Satisfaction: A Topic–Emotion Fusion Approach with Smartphone Data | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Effectively identifying factors related to user satisfaction is crucial for evaluating customer experience. This study proposes a two-phase analytical framework that combines natural language processing techniques with hierarchical decision-making methods. In Phase 1, an ERNIE-LSTM-based emotion model (ELEM) is used to detect fake reviews from 4016 smartphone evaluations collected from JD.com (accuracy: 84.77%, recall: 84.86%, F1 score: 84.81%). The filtered genuine reviews are then analyzed using Biterm Topic Modeling (BTM) to extract key satisfaction-related topics, which are weighted based on sentiment scores and organized into a multi-criteria evaluation matrix through the Analytic Hierarchy Process (AHP). These topics are further clustered into five major factors: user-centered design (70.8%), core performance (10.0%), imaging features (8.6%), promotional incentives (7.8%), and industrial design (2.8%). This framework is applied to a comparative analysis of two smartphone stores, revealing that Huawei Mate 60 Pro emphasizes performance, while Redmi Note 11 5G focuses on imaging capabilities. Further clustering of user reviews identifies six distinct user groups, all prioritizing user-centered design and core performance, but showing differences in other preferences. In Phase 2, a comparison of word frequencies between product reviews and community Q and A content highlights hidden user concerns often missed by traditional single-source sentiment analysis, such as screen calibration and pixel density. These findings provide insights into how product design influences satisfaction and offer practical guidance for improving product development and marketing strategies. | |
| 653 | |a Customer services | ||
| 653 | |a Customer satisfaction | ||
| 653 | |a Analytic hierarchy process | ||
| 653 | |a Deep learning | ||
| 653 | |a Food | ||
| 653 | |a Multiple criterion | ||
| 653 | |a Data mining | ||
| 653 | |a Product design | ||
| 653 | |a User generated content | ||
| 653 | |a Data envelopment analysis | ||
| 653 | |a Emotions | ||
| 653 | |a Feedback | ||
| 653 | |a Product development | ||
| 653 | |a Consumers | ||
| 653 | |a Design engineering | ||
| 653 | |a Sentiment analysis | ||
| 653 | |a User satisfaction | ||
| 653 | |a Clustering | ||
| 653 | |a Product reviews | ||
| 653 | |a User groups | ||
| 653 | |a Decision making | ||
| 653 | |a Quality of service | ||
| 653 | |a Electronic commerce | ||
| 653 | |a Natural language processing | ||
| 653 | |a Design factors | ||
| 700 | 1 | |a Li, Huan | |
| 700 | 1 | |a Xinyue, Mo | |
| 773 | 0 | |t Big Data and Cognitive Computing |g vol. 9, no. 5 (2025), p. 125 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3211858310/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3211858310/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3211858310/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |