A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods

The huge volume of user-generated data on social media is the result of the aggregation of users’ personal backgrounds, past experiences, and daily activities. This huge size of the generated data, the so-called “big data,” has been studied and investigated intensively during the past few years. In spite of the impression one may get from the media, a great deal of data processing has not been uncovered by existing techniques of data engineering and processing. However, very few scholars have tried to do so, especially from the perspective of multiple-criteria decision-making (MCDM). These MCDM methods can derive influence relationships and weights associated with aspects and criteria, which can hardly be achieved by traditional data analytics and statistical approaches. Therefore, in this paper, we aim to propose an analytic framework to mine social networks, feed the meaningful information via MCDM methods based on a theoretical framework, derive causal relationships among the aspects of the theoretical framework, and finally compare the causal relationships with a social theory. Latent Dirichlet allocation (LDA) will be adopted to derive topic models based on the data retrieved from social media. By clustering the topics into aspects of the social theory, the probability associated with each aspect will be normalized and then transformed to a Likert-type 5-point scale. Afterwards, for every topic, the feature importance of all other topics will be derived using the random forest (RF) algorithm. The feature importance matrix will be transformed to the initial influence matrix of the decision-making trial and evaluation laboratory (DEMATEL). The influence relationships among the aspects and criteria and influence weights can then be derived by using the DEMATEL-based analytic network process (DANP). The influence weight versus each criterion can be derived by using DANP. To verify the feasibility of the proposed framework, Taiwanese users’ attitudes toward air pollution will be analyzed based on the value–belief–norm (VBN) theory by using social media data retrieved from Dcard (dcard.tw). Based on the analytic results, the causal relationships are fully consistent with the VBN framework. Further, the mutual influences derived in this work that were seldom discussed by earlier works, i.e., the mutual influences between altruistic concerns and egoistic concerns, as well as those between altruistic concerns and biosphere concerns, are worth further investigation in future.