User Need Prediction Based on a Small Amount of User-Generated Content—A Case Study of the Xiaomi SU7
Abstract
:1. Introduction
2. Literature Review
2.1. User Need Mining and Predicting
2.2. UGC
3. Methodology
3.1. UGC Mining and Processing
3.1.1. UGC Mining
3.1.2. Product Features Extraction
3.1.3. User Need Clustering
3.2. Quantification of User Need Indicators
3.2.1. Calculation of User Attention
3.2.2. Calculation of User Satisfaction
3.3. Need Indicators Correction
3.3.1. Correlation Analysis Between User Need Indicators
3.3.2. Linear Regression Analysis Between User Need Indicators
3.3.3. Revision of User Need Indicators
3.4. Need Indicator Forecasts
4. Case Studies
4.1. UGC Mining and Processing for the Xiaomi SU7
4.1.1. UGC Mining for the Xiaomi SU7
4.1.2. Product Features Extraction and User Needs Clustering for the Xiaomi SU7
4.2. Quantification of User Need Indicators for the Xiaomi SU7
4.2.1. Calculation of User Attention for the Xiaomi SU7
4.2.2. Calculation of User Satisfaction for the Xiaomi SU7
4.3. Need Indicators Correction for the Xiaomi SU7
4.3.1. Correlation Analysis Between User Need Indicators for the Xiaomi SU7
4.3.2. Linear Regression Analysis Between User Need Indicators for the Xiaomi SU7
4.4. Need Indicator Forecasts for the Xiaomi SU7
5. Discussion
5.1. Model Validation
5.2. Analysis of Improvement Strategy of Product Design
6. Conclusions
7. Future Research Avenues
- (1)
- Firstly, augmenting the dataset to span a broader spectrum of products and user cohorts would enhance the universality of the research findings. This could entail collecting UGC from multiple platforms and regions, as well as integrating data from disparate sources, such as customer surveys and social media.
- (2)
- Secondly, it is imperative to enhance the prediction model by incorporating state-of-the-art machine learning algorithms and techniques. This could encompass exploring deep learning architectures for more accurate feature extraction and prediction, as well as devising hybrid models that amalgamate the strengths of diverse algorithms.
- (3)
- Finally, it is crucial to consider the impact of exogenous factors, such as technological advancements and regulatory modifications, on user needs. Future research should probe into how these factors interact with user needs and formulate corresponding product design and marketing adjustment strategies.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Author | Reference | Year | Research Methods and Contents |
---|---|---|---|
Liu et al. | [12] | 2018 | Proposed a visually-aware temporal rating model with topics using review text to help mine visual dynamics and non-visual features for a rating prediction task. |
Yusuf-Asaju et al. | [13] | 2019 | Employed machine learning algorithms for the prediction of customer satisfaction. |
Lee et al. | [14] | 2019 | Proposed predictive global sensitivity analysis based on user-generated reviews for the prediction of the demand for hyperdifferentiated products. |
Dou et al. | [15] | 2019 | Integrated a fuzzy Kano model and optimized gray model for the prediction of dynamic customer requirements and used House of Quality to calculate the optimal improvement plan. |
Ali et al. | [16] | 2020 | Proposed an ontology-based method to extract the design features of products from online customer reviews. |
Zhang et al. | [17] | 2020 | Used gray theory combined with Kansei engineering to mine the macro- and microscopic factors in product color design decision process based on the product color brand image. |
Jiang et al. | [18] | 2021 | Used opinion mining and a fuzzy time-series method to predict the weights of customer needs. |
Ostasz et al. | [19] | 2022 | Used the naïve Bayesian classifier for the prediction of the direction of product improvement. |
Cheng et al. | [20] | 2022 | Proposed a model based on the quantitative Kano model and the customer satisfaction degree for the prediction of the customer satisfaction degree of the new component scheme. |
Wang et al. | [21] | 2023 | Proposed an evolving demand satisfaction (EvoDESA) model to model a user’s demand evolution for next-basket prediction. |
Luo et al. | [22] | 2023 | Constructed an importance–satisfaction gap analysis (ISGA) model to obtain the changing trend of Chinese car users’ needs from UGC. |
Zhang et al. | [23] | 2023 | Used the gray–Markov model to predict the needs of automobile and mobile phone users from online reviews. |
Fan et al. | [24] | 2024 | Studied the data of e-commerce product recommendations from the perspective of user preference and association rules. |
Li et al. | [25] | 2024 | Developed a novel eye-tracking-based assessment tool to investigate user preference towards humanoid robot appearance design. |
Luo et al. | [26] | 2024 | Used the chaos theory and cvxEDA algorithm to extract the features of the front face of automobiles and study the relationship between them and user perception. |
Dinaryanti et al. | [27] | 2024 | Used topic modelling with the LDA algorithm and sentiment analysis with the naïve Bayes algorithm to analyze the factors that consumers consider when choosing smartphones. |
User Need | Product Feature Keywords |
---|---|
, , back row , , , , , , , | |
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | |
, , , , , , , , , , , , , , , , ,, , , , , soft , , ecology , , relaxed , science and technology , navigation , noise | |
, , shelter control panel , , , , , , , , , display screen , , , , , shock absorption , icebox , ventilate , , |
Periods | ||||
---|---|---|---|---|
1 | 0.0484 | 0.0733 | 0.0535 | 0.0290 |
2 | 0.0839 | 0.1453 | 0.0363 | 0.0685 |
3 | 0.0486 | 0.0797 | 0.0267 | 0.0340 |
4 | 0.0623 | 0.0612 | 0.0380 | 0.0256 |
5 | 0.0574 | 0.0769 | 0.0361 | 0.0439 |
Periods | ) | |||
---|---|---|---|---|
1 | 0.4542 | 0.5079 | 0.3595 | 0.3984 |
2 | 0.6084 | 0.8428 | 1 | 0.5547 |
3 | 0.5938 | 0.3029 | 0.5128 | 0.1476 |
4 | 0.2695 | 0.5776 | 0.7772 | 0.3972 |
5 | 0.4844 | 0.5298 | 0.5770 | 0 |
Average | Standard Deviation | |||||
---|---|---|---|---|---|---|
0.031 | 0.015 | 1 | ||||
0.066 | 0.016 | 0.917 * | 1 | |||
0.029 | 0.007 | 0.551 | 0.331 | 1 | ||
0.013 | 0.005 | 0.963 ** | 0.977 ** | 0.340 | 1 |
Average | Standard Deviation | |||||
---|---|---|---|---|---|---|
0.031 | 0.015 | 1 | ||||
0.066 | 0.016 | 0.781 | 1 | |||
0.029 | 0.007 | 0.591 | 0.935 * | 1 | ||
0.013 | 0.005 | 0.689 | 0.963 ** | 0.880 * | 1 |
Periods | ||||||||
---|---|---|---|---|---|---|---|---|
1 | 0.0281 | 0.0378 | 0.0535 | 0.0478 | 0.4542 | 0.4178 | 0.5161 | 0.5061 |
2 | 0.0591 | 0.0778 | 0.0363 | 0.1015 | 0.6084 | 0.6910 | 0.9781 | 0.8624 |
3 | 0.0327 | 0.0440 | 0.0267 | 0.0824 | 0.5938 | 0.804 | 0.3147 | 0.2975 |
4 | 0.0469 | 0.0679 | 0.0380 | 0.0734 | 0.2695 | 0.6362 | 0.6247 | 0.6348 |
5 | 0.0409 | 0.0871 | 0.0361 | 0.0785 | 0.4844 | 0.5944 | 0.5110 | 0.5827 |
Periods | ||||||||
---|---|---|---|---|---|---|---|---|
6 | 0.035 | 0.082 | 0.037 | 0.064 | 0.318 | 0.572 | 0.334 | 0.466 |
7 | 0.031 | 0.088 | 0.038 | 0.057 | 0.256 | 0.531 | 0.234 | 0.417 |
8 | 0.027 | 0.093 | 0.039 | 0.049 | 0.197 | 0.490 | 0.139 | 0.369 |
9 | 0.023 | 0.098 | 0.040 | 0.042 | 0.140 | 0.451 | 0.047 | 0.322 |
10 | 0.015 | 0.104 | 0.041 | 0.034 | 0.087 | 0.413 | −0.040 | 0.276 |
Feature Points | Connotation Explanation | Marking and Calculation |
---|---|---|
The ratio of the distance between the highest point of the spoiler and the height of the taillights to the height of the taillights. | ||
The ratio of the height of the taillights to the distance between the bottom of the trunk and the taillights. | ||
The ratio of the maximum height to the minimum height of the taillights. | ||
The angle of the outer corner of the taillights. | ||
The inner corner angle of the taillights. | ||
The edge corner angle of the taillights. |
Qin PLUS | Model 3 | Magotan | AION S | Hong Qi H5 | Xiaomi SU7 | |
---|---|---|---|---|---|---|
2.80 | 2.83 | 2.74 | 2.72 | 2.79 | 2.68 | |
0.42 | 0.44 | 0.42 | 0.46 | 0.43 | 0.48 | |
1.98 | 1.85 | 2.04 | 1.89 | 1.92 | 2.10 | |
144.25° | 145.02° | 144.28° | 144.04° | 143.56° | 147.56° | |
145.46° | 147.25° | 148.34° | 145.86° | 144.78° | 143.58° | |
45.96° | 47.25° | 46.87° | 49.25° | 48.36° | 43.94° |
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© 2024 by the authors. Published by MDPI on behalf of the World Electric Vehicle Association. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liu, L.; Ma, B. User Need Prediction Based on a Small Amount of User-Generated Content—A Case Study of the Xiaomi SU7. World Electr. Veh. J. 2024, 15, 584. https://doi.org/10.3390/wevj15120584
Liu L, Ma B. User Need Prediction Based on a Small Amount of User-Generated Content—A Case Study of the Xiaomi SU7. World Electric Vehicle Journal. 2024; 15(12):584. https://doi.org/10.3390/wevj15120584
Chicago/Turabian StyleLiu, Lingling, and Biao Ma. 2024. "User Need Prediction Based on a Small Amount of User-Generated Content—A Case Study of the Xiaomi SU7" World Electric Vehicle Journal 15, no. 12: 584. https://doi.org/10.3390/wevj15120584
APA StyleLiu, L., & Ma, B. (2024). User Need Prediction Based on a Small Amount of User-Generated Content—A Case Study of the Xiaomi SU7. World Electric Vehicle Journal, 15(12), 584. https://doi.org/10.3390/wevj15120584