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It is crucial for enterprises to clearly identify user needs during the process of formulating product design improvement plans. Therefore, it is essential to comprehensively and accurately identify user needs, explore the reasons behind the emergence of these needs, and incorporate user opinions into the process of product design improvement. A method is proposed to comprehensively and accurately capture user requirements and address the challenge of identifying the underlying causes of user requirements. This method utilizes online comments and operational data to identify user requirements and their influencing factors. First, text sentiment analysis techniques are employed to quantify the importance and performance values of product feature topics. Second, we construct a quadrant model to identify product features requiring improvement, and the original negative comments related to these features are traced. However, the quadrant model alone is insufficient to reflect specific product issues that users are concerned about. Therefore, a functional structure model based on product issues is designed to filter and identify factors that influence user requirements using operational data. Finally, a Bayesian network inference approach is utilized to identify the key influencing factors on user requirements, enabling analysis of the causes behind user requirements and the proposal of product design improvement strategies. The feasibility and effectiveness of the proposed method are validated through experiments conducted on heavy-duty truck data. By analyzing the original negative comments related to the power characteristics, specific user demands regarding the insufficient power of the product were identified, such as “obviously insufficient power when climbing slopes” and other issues. Based on the vehicle power system functional structure model, combined with expert knowledge and operational data, factors related to the state of parts and user behavior that may affect “insufficient vehicle power” were identified. Based on the analysis results, suggestions were made to improve the engine intake air temperature control strategy and to enhance vehicle performance by promoting correct user behavior through informational campaigns.

With the development of internet platforms, consumers increasingly rely on online product reviews during the shopping process. Customer online reviews can guide product design and provide valuable insights for businesses. Therefore, analyzing customer online reviews has become a research hotspot. However, existing studies suffer from some limitations, including the lack of fine-grained sentiment analysis and word clustering analysis. To address these issues, this study proposes a method based on Long Short-Term Memory (LSTM) for identifying and predicting customer preferences, aiming to provide more accurate information support for product design improvement personnel. This research first conducts data collection, preprocessing, and word vector computation. Subsequently, dictionary and sentiment labels are extracted. Based on a topic clustering system, sentiment labels are quantified for polarity classification, and sentiment scores and importance values are calculated as evaluation metrics for customer preference data. The preference-identified data are then applied to an LSTM prediction model, and the dataset is partitioned accordingly. Finally, the trained model is used for LSTM-based prediction of importance values and sentiment scores. The research results demonstrate that this method can accurately predict product preferences based on dynamic online reviews.

User comments provide valuable information for requirements analysis. To effectively extract requirements from user comments, it is important to determine informative user comments. However, existing studies have mainly focused on NLP-based methods for analyzing user comments, rather than defining a set of user comments to be analyzed. If target user comments are not clearly determined, duplicate requirements can be discovered or new requirements cannot be discovered. To tackle this problem, we present a new method which defines a set of target corpora from user comments. Our method automatically defines a set of target corpora to be analyzed by identifying underlying temporal changes in user comments. We applied our method to real-world user comments collected from a mobile application store. We confirmed that our method successfully defined a set of corpora which aids the effective requirements elicitation, and facilitated discovering new requirements while avoiding the derivation of redundant requirements.

This paper presents a study on how we can extract helpful review from customers and its effect on the early phase when designing a product. We present a framework for analyzing the reviews from online shopping sites by detecting helpful reviews, aspects, and top reviews. We conduct a through analysis on review comments of the Amazon site and form a novel framework which can automatically extract useful information from review documents and it can also collaboratively work between designers and opinion customers. Experimental results on helpful review identification and sentiment classification showed that the proposed model achieved promising results. We also conduct an interview with designers to assert whether or not the proposed framework is effective. The results showed that the proposed framework is helpful for designers.

One of the most challenging task in software development is developing software requirements. There are two types of software requirements — user requirement (mostly described by natural language) and system requirements (also called as system specifications and described by formal or semi-formal methods). Therefore, there is a gap between these two types of requirements because of inherently unique features between natural language and formal or semi-formal methods. We describe a semantic software engineering methodology using the design principles of SemanticObjects for object-relational software development with an example. We also survey other semantic approaches and methods for software and Web application development.

One of the most challenging task in software development is developing software requirements. There are two types of software requirements — user requirement (mostly described by natural language) and system requirements (also called as system specifications and described by formal or semi-formal methods). Therefore, there is a gap between these two types of requirements because of inherently unique features between natural language and formal or semi-formal methods. We describe a semantic software engineering methodology using the design principles of SemanticObjects for object-relational software development with an example. We also survey other semantic approaches and methods for software and Web application development.