Semantic spaces are used as a representation of language, capturing the meaning between linguistic units. These spaces are often built in large corpora requiring advanced equipment, specialized computational skills, and considerable effort. This project note will introduce and demonstrate the use of an accessible Shiny graphical interface allowing users to create semantic space models easily. Shiny is an R package in which one can program interactive web applications in R for others to interact with data or analyses. The advantage to Shiny applications is that naïve users can explore data without understanding the programming, and open sharing of code with the application can aid in learning the programming for one’s own use in their research. Within the application, users will be able to load popular semantic spaces or their own corpus for semantic space creation utilizing their preferred modeling technique, including LSA and TOPICS. A variety of user-friendly graphical tools, such as n-nearest neighbors or topic weighted graph, will further aid data visualization of the semantic network. Additionally, the application provides the calculation of cosine or simple co-occurrence, among other popular-relatedness values. This tool is intended for researchers who may not be programming-savvy, or as a teaching extension for psycholinguistics courses.

The media ecosystem has grown, and political opinions have diverged such that there are competing conceptions of objective truth. Commentators often point to political biases in news coverage as a catalyst for this political divide. The Moral Foundations Dictionary (MFD) facilitates identification of ideological leanings in text through frequency of the occurrence of certain words. Through web scraping, the researchers extracted articles from popular news sources' websites, calculated MFD word frequencies, and identified words' respective valences. This process attempts to uncover news outlets' positive or negative endorsements of certain moral dimensions concomitant with a particular ideology. In Experiment 1, the researchers gathered political articles from four sources. We were unable to reveal significant differences in moral or political endorsements, but we solidified the method to be employed in further research. In Experiment 2, the researchers expanded their number of sources to 10 and analyzed articles that pertain to two specific topics: the 2018 confirmation hearings of U.S. Supreme Court Justice Brett Kavanaugh and the partial U.S. Government Shutdown of 2018-2019. Once again, no significant differences in moral or political endorsements were found.

Partisan differences and diviseness have become an increasing hot topic in psychological research. Many theories have been proposed to explain these differences and divisions including Moral Foundations Theory. The current research seeks to use a linguistic measure of Moral Foundations, the Moral Foundations Dictionary (MFD), to test the theory in terms of predicted partisan differences. Through web scraping, we extracted articles from popular partisan news sources' websites, calculated MFD word frequencies, and identified words' respective valences. This process attempts to uncover news outlets' positive or negative endorsements of certain moral dimensions concomitant with a particular ideology. In Experiment 1, we gathered political articles from four sources. We were unable to reveal significant differences in moral endorsements, but we solidified the method to be employed in further research. In Experiment 2, we expanded their number of sources to 10 and analyzed articles that pertain to two specific topics: the 2018 confirmation hearings of U.S. Supreme Court Justice Brett Kavanaugh and the partial U.S. Government Shutdown of 2018-2019. Once again, no significant differences in moral endorsements were found. Together with past work, the results shed doubt on the validity of the MFD as a reliable measurement tool.

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