# A Novel Framework for Identifying Customers' Unmet Needs on Online Social Media Using Context Tree
## 7 Conclusions  
In this paper, the Context Tree is proposed as a new approach to identifying significant customers' unmet needs from online social media. The Context Tree consists of the primal keyword, its related keywords, and concept spaces. Each node of the Context Tree has a representative keyword and a corresponding concept space. So, the Context Tree is useful for structurally exploring the users' experience and context for the target product with respect to NPD. Besides, the proposed HSCS algorithm goes beyond simply searching for related keywords and identify the basis and evidence for customers' unmet needs. Therefore, this basis can be used to derive ideas for designing new functions or specifications for the target product, which is foremost in the NPD process. With the case study, users' contexts on five home appliances are identified using the Context Tree based on the HSCS algorithm. These contexts are used as sources to create users' scenarios for new functions and specifications of the target appliances. The main contribution of the Context Tree with HSCS algorithm is that the proposed method presented a way of utilizing online social media data, focusing on idea and concept screening stages in the early stages of the NPD process. According to [5], the idea screening stage and concept screening stage are critical to orienting the new product. The proposed method seeks not only the current user's experience of using the target product but also the context in which the product is not being used to find the basis for unmet needs. As a result of a case study for home appliances, including the oven, users' scenarios for the use of new products are successfully designed using evidence for customers' unmet needs identified by the Context Tree. Besides, as mentioned in previous sessions, the Context Tree with HSCS algorithm is not parametric, so it can be applied to big data to easily derive \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Combinations of Criteria**} & \multicolumn{3}{c}{**Average Score**} \\ \cline{2-4}  & **Non-experts** & **Experts** & **Total** \\ \hline Frequency of words (F) & 5.27 & 5.6 & 5.3525 \\ Degree of diffusion (D) & 5.07 & 3.6 & 4.7025 \\ Rating of experts and researchers (R) & 6.47 & 7.4 & 6.7025 \\ F + D & 5.6 & 6.2 & 5.75 \\ F + R & **7** & **8.8** & **7.45** \\ D + R & 6.4 & 6.8 & 6.5 \\ F + D + R & 6.27 & 7.8 & 6.6525 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of criteria combinations for extracting keywords. results. In addition, the derived result is composed of structural keywords and context, which has the advantage of easy interpretation. However, the proposed approach has some limitations because it still requires human effort to make ideas for new functions and specifications of a new product from the Context Tree. Besides, the process of extracting the users' context may be subjective. Obviously, it would be necessary for the data analyst to participate in finding out which topics are contained in the corpus containing keywords. In this case, the keyword structure can help you. For example, suppose that the primal keyword is 'oven'. After applying the pipeline to the corpus containing the primal keyword 'oven', it is found out that'snack' is the most frequent word. By executing pipelines, suppose that the words 'healthy','mom', and 'cookie' come up frequently in the corpus containing'snack'. I can combine these keywords to extract the users' context, like \"Mom usually wants to make a healthy cookie using the oven for her children\". The process of extracting the users' context may be subjective, but the materials, such as frequent keywords and the keyword structure used in the process, are objective through Big Data analysis. This is the biggest difference between conventional methods and the proposed method. However, it is important to reduce the user's involvement in the above process. Reducing the user's involvement will require further refinement of algorithms and analytical techniques.