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The rest of the paper is organized as follows: In Section 2 we provide an overview of Star Trek film and television series franchise. In Section 3 we introduce the LTO version 0.1.1. It is a hierarchically organized controlled vocabulary of themes, partitioned into the following four domains: the human condition, society, the pursuit of knowledge and alternate reality. There are 1535 unique themes in total. Criteria and guidelines motivating our hierarchical arrangement are discussed. In Section 4 we explain the hypergeometric testing approach to theme enrichment analysis in full technical detail. In Section 5 we use the hypergeometric test to identify enriched themes in the two Star Trek case studies outlined above. These results we compare with those obtained using the standard TF-IDF approach to enrichment analysis. We conclude the paper in Section 6 with a summary of our main contributions, a discussion of some limitations of our methodology and go on to describe a handful of possible future directions. Most notably, in terms of limitations, we emphasize that we manually annotated Star Trek episodes with themes, and as a consequence the findings we report inevitably reflect our point of view, and are not fully replicable. The theme enrichment analysis procedure based on the hypergeometric test is implemented in the R package stoRy (version 0.1.1) (Sheridan and Onsjö 2017), released through CRAN (The Comprehensive R Archive Network 2019). The thematically annotated Star Trek episode dataset is included in the package. A related R Shiny web application is available for download at the Theme Ontology GitHub repository (Theme Ontology Project GitHub Repository 2019).
We annotated each of the 280 episodes of TOS, TAS and TNG with themes in a similar manner. The process according to which we assigned themes can be summed up as follows. We independently annotated episodes with themes and then compared notes with a view toward building a consensus set of themes for each episode. We aimed to abide in the principle of low-hanging fruit in the compilation of consensus themes. In the present context, this means we tried to ensure that at least the most salient topics featured in the episodes are covered by appropriate themes. Another principle guiding our thought process is the minimization of false positives (i.e., the tagging of episodes with themes that are not featured) at the expense of tolerating false negatives (i.e., neglecting to tag episodes with themes that they feature). This strategy amounts to erring on the side of caution. Particular theme usages are motivated with brief comments in an effort justify their applicability. In addition to providing a checkable written record of the episode annotations, the practice of writing justifications for themes helps to reduce the risk of annotating episodes with nonapplicable themes. We fully acknowledge that this process needs more safeguards against the annotating of stories with themes that are idiosyncratic and unique to our point of view. We will return to this subject in the discussion section.
We used the test to identify enriched themes in TOS (120 themes), TAS (6 themes) and TNG (46 themes) at significance level 0.05. The background storyset in each case consists of the episodes from all the series combined. Here we report the outcomes of the analyses and show how they can aid in the generation of speculative hypotheses. In keeping with the Klingon case study from the previous subsection, we frame our hypotheses about TOS and TNG in terms of the top 20 most enriched themes for each respective series. This, we contend, is enough to convey the merits of our methodology without burdening the reader with protracted syntheses of long lists of enriched themes, however pleasant an exercise the formulation of such syntheses might be for the authors. On the other hand, we take some leeway and extend our analysis of TAS to the top 10 most enriched themes. But we note that our general conclusions would remain unchanged had we limited our interpretations to the 6 enriched themes at significance level 0.05. Tables containing the full results from these analyses are included in Supplementary Information File 2 (Sheridan and Onsjö 2019B). Star Trek enthusiasts will find few surprises in the kinds of themes that are shown to distinguish the respective series. To the layperson, however, the results of 5 may be unexpected and serve as a useful point of departure for exploring the series. The stacked percentage bar plots of Figure 3 show a broad pattern of human condition domain themes being enriched in TNG, alternate reality domain themes in TAS and society domain themes in TOS to some degree. The associated matrix scatterplot hints at some interesting enriched theme domain correlations between series. But let us proceed to inspect and compare more specific themes in order to gain a more nuanced understanding of the series.
Two main obstacles stand in the way of making our approach to theme enrichment analysis practical on a large-scale. First, a protocol for annotating stories with themes in such a manner that stories can be meaningfully compared in terms of their shared themes must be developed. We have taken a first step toward addressing this need by proposing a Basic Formal Ontology compliant draft theme ontology. Moving forward, we aim to integrate related ontologies such as the Emotion Ontology (Hastings et al. 2011) to name but one. Ontology design is an open-ended process, subject to setbacks and changes of direction. It is plain that our draft theme ontology will be no exception. However, we point out that even if the structure of the ontology changes markedly, many of the themes will remain intact as presently defined. Second, a large-scale database of compatibly themed stories is required. To this end, we have launched the Theme Ontology (beta version) online community platform (Theme Ontology 2019). The website features an ever-expanding controlled vocabulary of defined themes, hierarchically arranged into our draft theme ontology. Community members are encouraged to tag whatever stories (e.g., short stories, novels, films, TV shows, etc.) they please with themes drawn from the ontology, and adorn the ontology with newly coined themes as necessary. Stories are manually tagged with themes at present. Topic modeling techniques (Blei 2012) as implemented in such software packages as MALLET (McCallum 2002), the Python module gensim (Řhůřek and Sojka 2010), topicmodels (Grün and Hornik 2011) and the R package tm (Feinerer et al. 2008) have been used successfully to identify literary themes in text copora (Jockers 2013; Jockers and Mimno 2013; Goldstone and Underwood 2014; Boyd-Graber et al. 2017). In the future, we plan to use topic modeling to automatically collect themes for large numbers of stories in order to grow the Theme Ontology database. An interesting challenge awaits in figuring out how to adapt the current methods for automatic topic labeling (Lau et al. 2011; Cano Basave et al. 2014; Bhatia et al. 2017) to the problem of mapping identified topics to LTO themes. Lastly, a theme enrichment analyzer web application is available for download at the Theme Ontology GitHub repository (Theme Ontology Project GitHub Repository 2019). Tools from the stoRy package, including our theme enrichment test, will be made accessible as web applications on the Theme Ontology website in order to help users analyze curated thematic datasets. It is our aim to build up a large-scale database of freely available thematically annotated stories that can be analyzed using web applications within the Theme Ontology ecosystem. 2ff7e9595c