# A Novel Framework for Identifying Customers' Unmet Needs on Online Social Media Using Context Tree
## 2 Literature Review
### Studies in Various Business Fields Using Social Media Data  
[PERSON] and [PERSON] showed the effect of social media communication on consumer's perceptions of brands [18]. This study compared the impact of firm-created and user-generated social media communication on brand equity, brand attitude, and purchase intention. This study claimed that user-generated social media communication had a positive influence on both brand equity and attitude, while firm-created communication affected only brand attitude. [PERSON] and [PERSON] proposed attributes of social media marketing activities and examined the relationships among those perceived activities, such as value equity, relation equity, brand equity, customer equity, and purchase intention, through a structural equation model [26]. They claimed that value and relationship equity had significant positive effects on purchase intention. Some studies focused on extracting actionable topics, information, and knowledge from online social media data to solve business problems. [PERSON] and [PERSON] proposed a framework for modeling the topic of evolution and discovering emerging themes in social media [27]. [PERSON] and [PERSON] combined latent rating dimensions with latent review topic made by latent Dirichlet allocation (LDA) [28]. [PERSON] and [PERSON] extracted information on several smartphones from Twitter \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Monetary Cost** & **Time Cost** & **Sample Size** &
\begin{tabular}{c} **Risk of Exposing** \\ **the Intent of Questionnaire** \\ \end{tabular} \\ \hline
**Prototype** & \begin{tabular}{c} high \\ high \\ (relatively) medium \\ \end{tabular} & \begin{tabular}{c} high \\ high \\ high \\ \end{tabular} & \
\begin{tabular}{c} small \\ large \\ small \\ very small \\ \end{tabular} &
\begin{tabular}{c} very high \\ high \\ high \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Properties of conventional methods for identifying customer needs [10; 11; 20]. to predict product demand and longevity [29]. They analyzed tweets and figured out several strong, weak, and controversial features of each smartphone. Furthermore, they claimed a strong correlation between positive tweets and sales for each smartphone. [PERSON] and [PERSON] analyzed oral-care users' reviews on Amazon, spanning the period from 1996 to 2014. This study used word embedding and convolutional neural network (CNN) to classify informative and uninformative sentences from users' reviews. After that, non-repetitive sentences were identified through domain experts to derive the insights necessary for marketing actions [13]. [PERSON] et al. used latent Dirichlet allocation (LDA) to extract topics from user reviews on Reddit.com [30]. This study constructed a KeyGraph based on the co-occurrences among the topics. [PERSON] et al. compared the insights on electric vehicles derived through literature and the insights derived by analyzing tweets on Twitter [31]. [PERSON] et al. applied the topic modeling method, especially latent Dirichlet allocation (LDA), to users' reviews on Samsung Galaxy Note 5 to identify several topics and their sentiment weights [32]. [PERSON] and [PERSON] discussed how to use topic modeling methods in the marketing area [33]. Studies finding users' experiences on specific products in online social media are published. [PERSON] and [PERSON] searched keywords in tweets and blog posts related to smartphones. They figured out that there were unsatisfactory experiences for using smartphones in terms of multi-functionality and connectivity [11]. [PERSON] et al. analyzed user ratings and reviews on recliners on Amazon and found 15 clusters for affected users' experience of recliners [34]. [PERSON] et al. analyzed customer complaints about air-conditioners using feature selection and clustering to identify requirements at which customers want to obtain help from products [35]. [PERSON] et al. conducted a case study to analyze tweets about laptop brands.