## 2 Related Work
### Keyword Extraction  
The construction of the geolocation ontology discussed in Section 1 is primarily based upon the extraction of keywords from the short, textual reviews left by guests of each lodging on the Airbnb website. Despite their significance for analysis, indexing, and retrieval, those reviews do not have assigned keywords. A great number of document processing approaches rely on the manual assignment of keywords by professional curators, who may use a fixed taxonomy or the authors' judgment to provide a representative list. Therefore, the relevant research has been predominately focused on methods that automatically extract keywords from documents, as an aid either to suggest keywords for a professional indexer or to generate summary features for documents.  
Early approaches to automatic keyword extraction evaluate corpus-oriented statistics of individual words [18; 19]. In later research, similar metrics are used to select discriminating words as keywords for individual documents [20]. Corpus-oriented methods typically operate only on single words; this further limits the measurement of statistically discriminating words because they are often used in multiple and different contexts.  
To avoid these drawbacks, many keyword extraction methods operate on individual documents; that is, they extract keywords from a document, regardless of the current state of the corpus. Document-oriented methods therefore provide context-independent document features, enabling additional analysis that characterize changes within a text stream over time [21; 22]. In principle, the task of extracting keywords based on short-length text is challenging, as it is semantically sparse. For example, an algorithm that uses word co-occurrence in a single document is presented in [23], while in [24], keyword extraction is achieved using lexical chains that are composed of head nouns, which on their part, are derived from the representations of key phrases in the document.  
Graph-based approaches have also been extensively used for this task (i.e., the Twitter Keyword Graph [25]). TextRank, on the other hand, takes into account the lexical meaning of the text unit, as well [26; 27]. In [28], TextRank is extended in an unsupervised extractive summarization scheme that can examine whether there is any potential overlap between the extractive summarization and argument mining, while in [29], a system that applies a series of syntactic filters to identify part-of-speech tags is described, that is used to evaluate selected words as possible keywords.  
The negative effect of short text in the semantic sparseness of the obtained representations may be addressed using clustering techniques. In this setting, short excerpts are spliced into \"pseudo\"-long texts and subsequently topic-extraction techniques are used in order to identify keywords [30], like latent Dirichlet allocation [31] and latent semantic analysis [32]. A similar objective is achieved through the combination of document-oriented methods with machine learning techniques [33], like bi-directional long short-term memory networks [34], recurrent neural networks and neural language models [35].  
In an effort to achieve the best possible trade-off between performance, speed and efficiency, the rapid automatic keyword extraction (RAKE) [36] algorithm has been selected for the analysis that follows. RAKE is an unsupervised, domain and language independent method for keyword extraction from individual documents. More details on the algorithm and its hyper-parameters are provided in Section 5.1.