Liu Lingling, Ma Biao
School of Art and Design, Guilin University of Technology, Guilin, 541000, Guangxi, China.
Sci Rep. 2025 Mar 5;15(1):7716. doi: 10.1038/s41598-025-92173-8.
In the contemporary, fiercely competitive marketplace, companies must adeptly navigate the complexities of understanding and fulfilling user needs to succeed. By mining potential user needs from User Generated Content (UGC) on social media platforms, businesses can design products that resonate with users' needs, thereby swiftly capturing market share. When predicting user needs in this paper, the collected UGC is first processed through operations such as deduplication, word segmentation, and stop-word removal. Subsequently, Latent Dirichlet Allocation (LDA) is employed to extract product attribute features from UGC, cluster them to identify user needs and classify documents accordingly. The Bidirectional Encoder Representations from Transformers (BERT) model is then utilized for word vector feature extraction of the categorized documents, while also taking into account user interaction metrics to perform sentiment analysis of user needs using Long Short-Term Memory (LSTM). Finally, a Correlation Analysis-Vector Autoregressive-Markov (CA-VAR-Markov) model is constructed to forecast the evolution of user needs, and the Analytical Kano (A-Kano) model is applied for an in-depth analysis to propose strategies for product design optimization. In the case study, this paper takes the UGC from "Autohome" as an example to predict the user needs for the NIO EC6. Compared with LSTM and ARIMA, the prediction results are more accurate. Based on the prediction results and combined with the A-KANO model, suggestions are put forward for the optimization of the NIO EC6. The final results prove that the methods for identifying and predicting user needs proposed in this paper can effectively predict the development trend of user needs, providing a reference for enterprises to optimize their products.
在当代竞争激烈的市场环境中，企业若想取得成功，就必须熟练应对理解和满足用户需求的复杂性。通过挖掘社交媒体平台上用户生成内容（UGC）中的潜在用户需求，企业能够设计出符合用户需求的产品，从而迅速抢占市场份额。本文在预测用户需求时，首先对收集到的UGC进行去重、分词和停用词去除等操作。随后，使用潜在狄利克雷分配（LDA）从UGC中提取产品属性特征，对其进行聚类以识别用户需求并相应地对文档进行分类。接着利用来自Transformer的双向编码器表示（BERT）模型对分类后的文档进行词向量特征提取，同时考虑用户交互指标，使用长短期记忆（LSTM）对用户需求进行情感分析。最后，构建相关分析-向量自回归-马尔可夫（CA-VAR-马尔可夫）模型来预测用户需求的演变，并应用分析卡诺（A-卡诺）模型进行深入分析，以提出产品设计优化策略。在案例研究中，本文以“汽车之家”的UGC为例，预测蔚来EC6的用户需求。与LSTM和ARIMA相比，预测结果更准确。基于预测结果并结合A-KANO模型，对蔚来EC6的优化提出了建议。最终结果证明，本文提出的识别和预测用户需求的方法能够有效预测用户需求的发展趋势，为企业优化产品提供参考。
