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.
Moving beyond the dominant bag-of-words approach to sentiment analysis we introduce an alternative procedure based on distributed word embeddings. The strength of word embeddings is the ability to capture similarities in word meaning. We use word embeddings as part of a supervised machine learning procedure which estimates levels of negativity in parliamentary speeches. The procedure's accuracy is evaluated with crowdcoded training sentences; its external validity through a study of patterns of negativity in Austrian parliamentary speeches. The results show the potential of the word embeddings approach for sentiment analysis in the social sciences.
The chapter makes one conceptual and one empirical contribution to the study of the elite–masses gap in European integration. While most research focuses on substantive representation of voter opinions by MPs, under the ‘issue congruence paradigm’ we consider the entire chain of delegation from voters, to MPs, to governments. Representation gaps are measured as resulting from the two-step aggregation process of preferences typical for party democracies (first step within and the second between political parties). Specifically, the chapter looks at key projects towards a fully integrated Europe, such as common European foreign-, defence-, social security-, and tax-policy, and EU cohesion policy, that are already in place, agreed in principle, or are prominent when deepening European integration is the aim. Of the 15 countries covered in this book, the gaps are particularly large in Britain, Denmark, Germany, and Austria in Western Europe, and Estonia and Poland in Eastern Europe.
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