 Signals of Public Opinion in Online CommunicationSandra González-Bailón and Georgios Paltoglou The ANNALS of the American Academy of Political and Social Science, 2015, vol. 659, issue 1, 95-107 Abstract: This study offers a systematic comparison of automated content analysis tools. The ability of different lexicons to correctly identify affective tone (e.g., positive vs. negative) is assessed in different social media environments. Our comparisons examine the reliability and validity of publicly available, off-the-shelf classifiers. We use datasets from a range of online sources that vary in the diversity and formality of the language used, and we apply different classifiers to extract information about the affective tone in these datasets. We first measure agreement (reliability test) and then compare their classifications with the benchmark of human coding (validity test). Our analyses show that validity and reliability vary with the formality and diversity of the text; we also show that ready-to-use methods leave much space for improvement when analyzing domain-specific content and that a machine-learning approach offers more accurate predictions across communication domains. Keywords: content analysis; text mining; sentiment analysis; language formality; information diversity; lexicon-based methods; machine learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:anname:v:659:y:2015:i:1:p:95-107 Access Statistics for this article More articles in The ANNALS of the American Academy of Political and Social Science |
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