# A Novel Framework for Identifying Customers' Unmet Needs on Online Social Media Using Context Tree
## 1 Introduction  
In respect of user-centered design (UCD), customer needs contribute significantly to determining the initial direction of the new product development (NPD) process [1]. Customer needs refer to the various attributes of the potential product that the customer requires, also known as customer attributes or customer requirements. Considering customers' unmet needs in the early stages of the NPD process, the design direction of new functions and specifications for the new product can be quickly set [2; 3]. In addition, many studies have claimed that it is important to constantly grasp customers' unmet needs and consider them in the full-cycle NPD process [4; 5; 6; 7]. Companies attempt to identify customer needs through various channels, such as prototype, market research, focus group interview (FGI), and user observation [8; 9; 10]. These methods generate knowledge through question and answer between experimenter and user. However, these methods have several drawbacks. The answers of participants tend to be induced by the experimenter [11]. In addition, the survey method does not even provide hidden meaning to the basis of the results [12]. Recently, online social media has become an important area for investigating customer preferences [13]. Online social media is defined as services that enable users to establish online connections with their friends through the application and share information with them [14]. It has great potential to generate new knowledge and business value [15]. There is increasing use of social media data to derive customer needs. In marketing, some studies that attempt to extract insights for promotional strategies from social media are underway [16; 17; 18]. To derive perceptions of people, the sentiment analysis methods can be applied to social media text [19]. These studies focus oncalculating the number or ratio of keyword frequencies or latent topics in retrospectively collected data. However, while these studies are useful for finding customer context retrospectively, it is more important to find needs that do not satisfy customers in terms of NPD and UCD. This study proposes a quantitative approach to identifying customers' unmet needs in online social media, such as social network services (SNSs) or blog services. The proposed method is Context Tree, which is a tree structure expressing user context while searching for related keywords hierarchically. Each node of the Context Tree corresponds to a representative keyword and its related keywords. Various experiences of users with keywords are summarized in each node. On the business side, users' summarized experiences can be used to derive the new features or specifications of new products. To create the Context Tree, it is necessary to collect a large amount of text data from online social media and pre-process the data using natural language processing (NLP) techniques. The branches of the Context Tree are expanded by the Hierarchical Search of Concept Spaces (HSCS) algorithm, which is proposed by this study. The HSCS algorithm's purpose is to find unmet needs for the target product in online social media data, especially text data. This algorithm figures out hierarchically summarized user experiences for the target product \(p\) of the NPD process through related keywords to \(p\). Furthermore, it links user experiences, which are not relevant to \(p\) and are relevant to \(p\)'s-related keywords. The specific process of the HSCS algorithm is described in Section 3.2. To verify the Context Tree generated by the HSCS algorithm, the proposed framework is used to discover ideas for implementing the NPD process for major home appliances. Target online social media data includes tweets on Twitter and blog posts on Korean web portals. The remainder of this paper is organized as follows. In Section 2, several studies on market research, idea generation, and social media data are summarized. In Section 3, the Context Tree proposed in this study is introduced.