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

Martin Haselmayer () and Marcelo Jenny ()
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Martin Haselmayer: University of Vienna
Marcelo Jenny: University of Vienna

Quality & Quantity: International Journal of Methodology, 2017, vol. 51, issue 6, No 13, 2623-2646

Abstract: 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.

Keywords: Sentiment analysis; Crowdcoding; Political communication; Negative campaigning; Media negativity (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (22)

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DOI: 10.1007/s11135-016-0412-4

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