## 2 Literature Review
### Text Mining  
Text-mining algorithms may take one of two broad approaches: supervised [35] or unsupervised [36]. A high-level overview of a supervised text-mining method is illustrated in Figure 1. In a supervised text-mining setting, a human coder is first required to annotate/label manually a sample of the documents against a pre-defined list of categories. The text-mining algorithms are then trained on the manually annotated samples and learn to associate the textual content of a document with its underlying label (e.g., textual content expresses positive/negative sentiment, or relevance to politics/sports/news/technology). Once the training process is complete, the algorithm is used to categorize any unlabeled documents automatically to the same pre-defined list of categories. A supervised algorithm can substantially reduce the time needed to analyze a large document collection, whereby a human coder manually categorizes a sample of the documents (normally a small percentage of the whole data), and the remaining unlabeled ones are automatically categorized by the trained text-mining algorithm [37]. By contrast, unsupervised approaches (illustrated in Figure 2) aim to identify the hidden patterns in a collection of documents (e.g., clusters of semantically similar documents or latent topics) and do not involve a training phase (no need for manual labeling), so can be readily applied to any unlabeled document collection. Unsupervised text-mining methods are widely used to facilitate exploratory analyses of document collections by extracting information that is not immediately evident to experts [38; 39]. For example, the most commonly used unsupervised training approaches use document clustering [40] or topic modeling [41]. Systematic analysis of open-ended survey responses involves a considerable workload in terms of manual annotation; therefore, open-ended responses are often excluded from  
Figure 1: High-level overview of a supervised text-mining approach. Figure 2: High-level overview of an unsupervised text-mining approach. survey experiments [42; 43; 44]. Nonetheless, survey researchers recognize the importance of analyzing open-ended data, pointing out that such free-text responses to open-ended questions record detailed and useful information that may not be captured by close-ended questions [45; 46]. To reduce the workload associated with processing open-ended questions, several studies have explored the use of text mining to automate the underlying analysis process. Reference [47] presented one of the earliest text-mining approaches to the analysis of a dataset of open-ended responses. In their study, the data were collected from a survey that investigated employees' work-related perceptions of working in a large corporation. A dictionary-based text-mining method was used to assign a sentiment score automatically to each free-text response (a high score indicating a positive sentiment, and a low score a negative sentiment). The results achieved a high correlation between the automatically computed sentiment scores of the survey's responses to the open-ended questions and the quantitative scores of responses to its close-ended questions. Reference [43] used an unsupervised text-mining method, namely a term-clustering model, to identify thematically the coherent groups of the terms used in responses to open-ended survey questions. For evaluation purposes, their method was applied to a large-scale dataset of approximately 2000 free-text responses about consumers' preferences. The results showed that the method was able to identify informative and meaningful clusters of the terms that were discussed in the free-text comments. In another study [44], a statistical topic-modeling method (i.e., an unsupervised text-mining approach) was developed to analyze the topical content of open-ended data automatically. The topical content of open-ended data was represented by a finite set of topics and each, in turn, was represented by a finite set of words.