The Evaluative Lexicon: Adjective use as a means of assessing and distinguishing attitude valence, extremity, and emotionality

Rocklage, Matthew D.; Fázio, Russell H.

doi:10.1016/j.jesp.2014.10.005

Cited by 63 publications

(100 citation statements)

References 21 publications

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“…Very high positive correlations emerged in both samples (rs > .92). It should be stressed that a similar kind of measure has been used recently by Rocklage and Fazio (2015) in order to measure the valence and the emotionality of attitudes. 2.…”

Section: Notesmentioning

confidence: 99%

Compensation is for real: Evidence from existing groups in the context of actual relations

Cambon

Yzerbyt

2016

Group Processes & Intergroup Relations

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Compensation refers to the fact that a group perceived as higher than another on one of the fundamental dimensions of social judgment (competence and warmth) is also perceived as lower than the other group on the other dimension. Relying on a full-crossed design, the present work tested compensation in a real-life situation using existing groups involved in an ongoing relation. As predicted, compensation emerged when (a) the difference between the groups, and thus the perceived legitimacy of the status difference, was large as opposed to small, and (b) the relation between the groups was asymmetrical. In contrast, the smaller the difference (the lesser the legitimacy), the more ingroup bias emerged.

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Section: Notesmentioning

confidence: 99%

Compensation is for real: Evidence from existing groups in the context of actual relations

Cambon

Yzerbyt

2016

Group Processes & Intergroup Relations

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“…One approach is to examine the language a person uses to convey that attitude. Indeed, we could easily convey our attitude toward something by simply saying that we Blike^or Bdislike^it, but instead we have a myriad of words to communicate our attitude, from Bvaluable^to Bamazing^and from Bobjectionable^to Bterrible.^On the basis of this observation, Rocklage and Fazio (2015) developed the EL to use these differences in language to measure whether an individual has an attitude that is more emotional versus cognitive in nature.…”

Section: The Evaluative Lexiconmentioning

confidence: 99%

“…To quantify individuals' language for its emotionality, Rocklage and Fazio (2015) generated a list of 94 adjectives-for example, Bvaluable,^Bfantastic,^and Bmagnificent^-and obtained normative ratings from a set of judges with respect to the emotionality implied by each adjective, as well as its implied valence and extremity (the deviation from the midpoint of the valence scale). For instance, on the basis of these normative ratings, an individual who used the adjective Bfantastic^would score a 6.64 out of 9.00 on emotionality, 8.57 out of 9.00 on positivity, and 4.07 out of 4.50 on extremity.…”

Section: The Evaluative Lexiconmentioning

confidence: 99%

“…BWonderful^signals an evaluation based relatively more on emotion. Moreover, differences in extremity and emotionality account for unique variance when predicting outcomes of interest (Rocklage & Fazio, 2015. Thus, a strength of the EL is that it can provide measures of both the emotionality and extremity of individuals' attitudes, even with a single word.…”

Section: The Evaluative Lexiconmentioning

confidence: 99%

“…Over the last decade, the rapid expansion of the Internet and the availability of vast repositories of natural text have allowed researchers to systematically study individuals' reactions, opinions, and behavior on a massive scale. Due to this expansion, an immense opportunity now exists to study human responses and behavior Bin the wild.^As part of the effort to quantify responses in natural text, Rocklage and Fazio (2015) developed and introduced a novel computational tool called the Evaluative Lexicon (EL). The EL utilizes natural language to quantify the degree to which an individual's attitude or reaction is based on emotion (its emotionality), whether the reaction is positive or negative (its valence), and the extent of that positivity or negativity (its extremity).…”

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confidence: 99%

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The Evaluative Lexicon 2.0: The measurement of emotionality, extremity, and valence in language

2017

Self Cite

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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 .

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How online word‐of‐mouth impacts receivers

Moore

Lafreniere

2019

Consumer Psychology Review

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Online word‐of‐mouth (WOM) can impact consumers’ product evaluations, purchase intentions, and choices—but when does it do so? How do those receiving WOM know whether to rely on a particular message? This article suggests that the multiple players involved in online WOM (receivers, senders, sellers, platforms, and other consumers) each have their own interests, which are often in conflict. Thus, receivers of WOM are faced with a judgment task in deciding what information to rely on: They must make inferences about the product in question and about the players who provide or present WOM. To do so, they use signals embedded in various components of WOM, such as average star ratings, message content, or sender characteristics. The product and player information provided by these signals shapes the impact of WOM by allowing receivers to make inferences about (a) their likelihood of product satisfaction, and (b) the trustworthiness of WOM players, and therefore the trustworthiness of their content. This article summarizes how each player changes the impact of online WOM, providing a lens for understanding the current literature in online WOM, offering insights for theory in this context, and opening up pathways for future research.

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The Evaluative Lexicon: Adjective use as a means of assessing and distinguishing attitude valence, extremity, and emotionality

Cited by 63 publications

References 21 publications

Compensation is for real: Evidence from existing groups in the context of actual relations

Compensation is for real: Evidence from existing groups in the context of actual relations

The Evaluative Lexicon 2.0: The measurement of emotionality, extremity, and valence in language

How online word‐of‐mouth impacts receivers

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