Marcelo Jenny

Sentiment analysis of political communication: combining a dictionary approach with crowdcoding

Abstract Sentiment is important in studies of news values, public opinion, negative campaigning or political polarization and an explosive expansion of digital textual data and fast progress in automated text analysis provide vast opportunities for innovative social science research. Unfortunately, tools currently available for automated sentiment analysis are mostly restricted to English texts and require considerable contextual adaption to produce valid results. We present a procedure for collecting fine-grained sentiment scores through crowdcoding to build a negative sentiment dictionary in a language and for a domain of choice. The dictionary enables the analysis of large text corpora that resource-intensive hand-coding struggles to cope with. We calculate the tonality of sentences from dictionary words and we validate these estimates with results from manual coding. The results show that the crowdbased dictionary provides efficient and valid measurement of sentiment. Empirical examples illustrate its use by analyzing the tonality of party statements and media reports.

Measuring the “Europeanization” of Austrian Law-Making: Legal and Contextual Factors

In this chapter we subject Austrian legislation to two different approaches of measuring the extent of Europeanization. We begin by outlining the political and legal framework of legal Europeanization in Austria. Next, we address the Europeanization of lawmaking, that is, the impact of EU membership on the national laws passed immediately before and since Austria’s accession. We show the degree of Europeanization of Austrian legislation as identified by (1) manual coding and (2) by automatic coding with a keyword approach. We show how the keyword measure fares when interpreted as a substitute for the former.