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The rapid expansion of the Internet and the availability of vast repositories of natural text provide researchers with the immense opportunity to study human reactions, opinions, and behavior on a massive scale. To help researchers take advantage of this new frontier, the present work introduces and validates the Evaluative Lexicon 2.0 (EL 2.0)-a quantitative linguistic tool that specializes in the measurement of the emotionality of individuals' evaluations in text. Specifically, the EL 2.0 utilizes natural language to measure the emotionality, extremity, and valence of evaluative reactions and attitudes. The present article describes how we used a combination of 9 million real-world online reviews and over 1,500 participant judges to construct the EL 2.0 and an additional 5.7 million reviews to validate it. To assess its unique value, the EL 2.0 is compared with two other prominent text analysis tools-LIWC and Warriner et al.'s (Behavior Research Methods, 45, 1191-1207, 2013) wordlist. The EL 2.0 is comparatively distinct in its ability to measure emotionality and explains a significantly greater proportion of the variance in individuals' evaluations. The EL 2.0 can be used with any data that involve speech or writing and provides researchers with the opportunity to capture evaluative reactions both in the laboratory and "in the wild." The EL 2.0 wordlist and normative emotionality, extremity, and valence ratings are freely available from www.evaluativelexicon.com .

Researchers, marketers, and consumers often believe that amplifying emotional content is impactful for the spread of information and purchasing decisions. However, there is little systematic investigation of when emotionality backfires. This research demonstrates when and why positive emotion can have enhancing versus backfiring effects. The authors find that reviewers who express greater positive emotion are indeed more positive toward their products, regardless of product type. In addition, expressed emotion for hedonic products has a positive impact when read by others, but this emotion backfires for utilitarian products, leading others to be less positive. The authors construct a conceptual model of these effects and show that violated expectations leading to decreased trust underlie this divergence between reviewers and readers. The effects occur in well-controlled experiments as well as computational linguistic analysis of 100,000 Amazon reviews across 500 products. Indeed, emotional reviews of utilitarian products are less likely to become popular and be displayed on the product’s front page on Amazon. This work also introduces a novel tool for quantifying natural language in marketing: the Evaluative Lexicon.

Persuasion is a foundational topic within psychology, in which researchers have long investigated effective versus ineffective means to change other people's minds. Yet little is known about how individuals' communications are shaped by the intent to persuade others. This research examined the possibility that people possess a learned association between emotion and persuasion that spontaneously shifts their language toward more emotional appeals, even when such appeals may be suboptimal. We used a novel quantitative linguistic approach in conjunction with controlled laboratory experiments and real-world data. This work revealed that the intent to persuade other people spontaneously increases the emotionality of individuals' appeals via the words they use. Furthermore, in a preregistered experiment, the association between emotion and persuasion appeared sufficiently strong that people persisted in the use of more emotional appeals even when such appeals might backfire. Finally, direct evidence was provided for an association in memory between persuasion and emotionality.

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