1College of Management, University of Massachusetts, Boston, MA, USA. 2Kellogg School of Management, Northwestern University, Evanston, IL, USA.
People have always looked to and relied on the opinions of
those around them to make decisions1,2. Now, the rise and pro-
liferation of online crowd-sourced platforms, such as Yelp and
Glassdoor, have fundamentally transformed the scope and speed
with which people can harness others’ assessments. Given their
scale, openness and availability, these platforms promise to facilitate
people’s ability to find the best option3,4. Indeed, rather than rely on
trial and error or small, informal networks, people have immedi-
ate access to the experience and wisdom of crowds. In the case of
movies and restaurants, for instance, this aggregated wisdom should
help quickly identify success—those items that have thrived and
become popular. For most platforms, the primary means to identify
successful goods is through an aggregated ‘star rating’—a numeric
rating that measures the extent to which people’s opinions are rela-
tively positive versus negative.
Perhaps surprisingly, a striking limitation of these online rating
systems has emerged: reviews are overwhelmingly positive5. On
Amazon.com, for example, the average star rating is approximately
4.2 out of 5, with well over half of the reviews being 5-star ratings6,7.
Nearly half of all Yelp reviews are 5-star ratings8, and recent research
indicates that nearly 90% of Uber ratings may be 5 stars9. A visual
representation of most online ratings reveals a J-shaped distribution,
with many 4- and 5-star ratings, a few 1-star ratings and few ratings
in between5. The degree of overwhelming positivity suggests that
individuals are often confronted with choosing between numerous
items with similar star ratings, especially given that people will not
even consider options that garner less than a 3-star rating.
A principal problem with this degree of positivity is that the rat-
ings themselves may ultimately be an unreliable indicator of the
success of that item and the human behaviour that underlies this
success (for example, restaurant reservations). Specifically, two
items might receive nearly identical ratings but vary vastly in their
success. Indeed, past research has shown substantial variability
in the link between the positivity of individuals’ ratings and suc-
cess10–12. For example, the positivity of online ratings shows little
association with the underlying quality of products and fails to pre-
dict their resale value13. Moreover, an analysis of over 400 movies
revealed that greater positivity in online ratings was associated with
fewer people attending a movie, as evidenced by lower box office
revenue14. This problem has even led companies such as Netflix to
abandon standard rating systems due to their poor performance15.
Put simply, these ratings seem not to hold the wisdom that people
believe they do.
Across disciplines, behavioural scientists are beginning to recog-
nize the problematic nature of these ratings. That is, given this large
degree of positivity, a number of cases exist where items receive a
similarly positive rating. Yet, when it comes to human behaviour,
substantial differences exist—not all 5-star restaurants are equally
popular. The high degree of positivity effectively makes the ratings
ineffective signals for discriminating what are likely to be the best or
most successful options. We label this challenge to discern success
within the mass of positive ratings the ‘positivity problem’.
Although quantitative ratings are the most salient and accessible
output of online reviews, most crowd-sourced platforms include a
written portion where people provide qualitative assessments. As
technology has improved, researchers have embraced computa-
tional social science techniques to quantify these qualitative assess-
ments. Perhaps the most common method to analyse text in this
way is via sentiment analysis, which most often quantifies language
in terms of its positivity16. Some words suggest greater favourability
(for example, the word ‘liked’), whereas others suggest greater nega-
tivity (for example, ‘disliked’).
Computational social science has focused primarily on the
positivity (also known as valence) of people’s attitudes17. Relatively
few efforts have sought to quantify aspects of individuals’ attitudes
beyond positivity18. Nevertheless, social psychologists have long
acknowledged that positivity is not always a reliable predictor of
behaviour17,19. To address the limitations of positivity, scholars have
introduced and explored additional facets of an attitude that can
improve its predictive ability17,20. One such facet is the emotionality
of an attitude—the extent to which an attitude is based on individu-
als’ feelings or emotional reactions21–24. Positivity and emotionality
are conceptually and empirically distinct. For example, the words
‘enjoyable’ and ‘impeccable’ imply very similar levels of positivity,
but research indicates that the word ‘enjoyable’ is likely to be indica-
tive of a more emotional attitude than the word ‘impeccable’25.
Mass-scale emotionality reveals human behaviour
and marketplace success
Matthew D. Rocklage 1 ✉ , Derek D. Rucker2 and Loran F. Nordgren2
Online reviews promise to provide people with immediate access to the wisdom of the crowds. Yet, half of all reviews on Amazon
and Yelp provide the most positive rating possible, despite human behaviour being substantially more varied in nature. We term
the challenge of discerning success within this sea of positive ratings the ‘positivity problem’. Positivity, however, is only one
facet of individuals’ opinions. We propose that one solution to the positivity problem lies with the emotionality of people’s opin-
ions. Using computational linguistics, we predict the box office revenue of nearly 2,400 movies, sales of 1.6 million books, new
brand followers across two years of Super Bowl commercials, and real-world reservations at over 1,000 restaurants. Whereas
star ratings are an unreliable predictor of success, emotionality from the very same reviews offers a consistent diagnostic sig-
nal. More emotional language was associated with more subsequent success.
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Articles NAtUre HUMAN BeHAviOUr
Moreover, the emotionality of individuals’ attitudes can now be cap-
tured via text analysis25,26.
Attitudes based more on emotion tend to be stronger and more
predictive of behaviour. In the political domain, voters’ emotional
reactions to a political candidate—compared with their more cogni-
tive reactions—were better predictors of future voting behaviour27.
Attitudes based more on emotion also tend to come to mind more
quickly28, are more extreme25,26 and are more consistent across con-
texts29 and time30. One reason for this relationship is that emotions
provide individuals themselves with an indication that something
especially impactful has occurred31,32, and they can thereby act as
a particularly clear signal to individuals regarding their own atti-
tude28,33,34. This strong signal, in turn, can lead attitudes to be held
more strongly in memory28, which is an established predictor of the
impact and durability of an attitude17,35.
Outward displays of emotion also signal the importance of one’s
attitude to others. The social–functional approach to emotion
puts forth that a primary function of emotion is to communicate
the strength of one’s attitudes, desires and intentions36–38. As social
animals, understanding others’ goals and intentions is vital for suc-
cessful social coordination. Displays of joy and anger, for instance,
provide others with strong signals regarding a person’s state of
mind, goals and priorities. In the context of negotiation, expressions
of happiness signal that one is open to concession, whereas displays
of anger signal that one is unlikely to compromise39,40. These find-
ings indicate that when humans use emotion online, it is probably a
signal that an experience was particularly impactful to them.
Taken together, research suggests that attitudes based on emo-
tion are stronger and more predictive of one’s own behaviour, and
that people use emotion to communicate the impact of their experi-
ence to others. The consequence is that emotionality in text may be
more indicative of the success of a product or service. To illustrate,
consider a restaurant. From an attitudinal perspective, the ability of
a restaurant to elicit a positive, emotional, feelings-based reaction
is likely to lead to a more strongly held attitude in the individual.
This stronger attitude could lead that restaurant to come to mind
more frequently in the future and lead the individual to be more
likely to visit again. From a social–functional perspective, individu-
als’ emotional reactions may also signal to others just how impactful
an experience was and thereby generate more attention for that res-
taurant from others. Thus, for both these reasons, more emotional
language may be able to predict success where star ratings cannot.
In short, we argue that capturing the emotionality expressed in
online reviews may offer one solution to the positivity problem.
More specifically, we hypothesize that the emotionality of people’s
online reviews can predict success and the mass-scale human behav-
iour that underlies this success where aggregated online ratings do
not. In providing evidence of both the positivity problem and the
relationship between emotionality and mass-scale human behav-
iour across multiple domains, we aim to accomplish two objec-
tives. First, we demonstrate the breadth of the positivity problem.
Second, we offer one solution to this problem using a theory-based
approach. In doing so, this work also advances our understanding
of emotionality—a construct considered of great importance across
the social sciences32—by revealing that it has the ability to predict
mass-scale behaviour and marketplace success.
Study 1. In Study 1, we predicted human behaviour and success
in the movie industry in the form of box office revenue earned in
the United States. We obtained all online reviews for all movies
from Metacritic.com from 2005 to 2018—13 years of data—and
used the first 30 user reviews written for each movie to measure
the movie’s star rating (0 to 10 stars) and text emotionality. We
also measured the valence (that is, positivity) of the text to assess
the unique contribution of emotionality. We selected the first 30
reviews for two reasons. First, using the first reviews written for a
movie helped avoid a situation where the success of the movie is
already known by reviewers, which can influence how individu-
als write about the movie41. Second, this approach helped ensure
that reviewers were expressing their own opinions as opposed
to echoing the consensus viewpoint of others. Prior work indi-
cates that early reviews can systematically bias subsequent post-
ing behaviour both in the real world and in well-controlled
laboratory experiments42,43. By using early reviews, we sought to
avoid these influences. Moreover, we used this same number of
reviews consistently in all applicable studies. These results were
also robust when using an alternative number of reviews (that is,
the first 40 reviews) and when using all possible reviews (see the
Supplementary Results for Study 1).
Across all studies, we used the Evaluative Lexicon (www.evalu-
ativelexicon.com) to quantify the average valence and emotional-
ity expressed25,26. Specifically, the Evaluative Lexicon measures the
valence and emotionality implied by the words that individuals
use (for example, ‘amazing’ or ‘enjoyable’). It has been directly vali-
dated as a measure of the valence and emotionality of individuals’
attitudes with both well-controlled laboratory experiments and
real-world naturalistic text25,26. While past work using the Evaluative
Lexicon has focused on the relationship between emotionality and
star ratings25,26, that work did not examine emotionality’s unique
relation with mass-scale behaviour, above and beyond emotional-
ity’s connection with star ratings. As overviewed earlier, while there
is a relationship between emotionality and individuals’ positivity,
these are separable constructs. Unless noted otherwise, all results
across studies utilize multiple regression with standardized coef-
ficients (B), log-transformed dependent variables and two-tailed
As evidence for the large number of positive ratings on this plat-
form, 81% of movies were rated positively (that is, they received an
average star rating above the midpoint of 5 stars). Given that our
aim is to predict success and human behaviour within a sea of posi-
tive reviews, our analyses examined whether emotionality was pre-
dictive of box office revenue for movies that were judged as positive
(those rated above 5 stars on average). There were 2,383 movies.
We first assessed whether the movie’s average star rating was
predictive of its box office revenue. A movie’s star rating was pre-
dictive of a movie making less box office revenue (B = −0.08;
t(2,381) = 3.24; P = 0.001; 95% confidence interval (CI), (−0.136,
−0.033)). When all movies were included—even those with an ini-
tial negative rating—star ratings were not significantly predictive
of box office revenue (B = 0.004; t(2,931) = 0.15; P = 0.88; 95% CI,
We then added the average emotionality of the reviews’ text
to this same model and the average text valence as a control. Star
ratings continued to be a significant negative predictor of the
movie’s box office revenue (B = −0.13; t(2,379) = 3.86; P < 0.001;
95% CI, (−0.193, −0.063); Fig. 1, left panel), and text valence was
in the positive direction but ultimately non-significant (B = 0.06;
t(2,379) = 1.78; P = 0.07; 95% CI, (−0.006, 0.124)). Of the great-
est importance, beyond these effects, emotionality was a sig-
nificant positive predictor of future box office revenue (B = 0.08;
t(2,379) = 3.01; P = 0.003; 95% CI, (0.027, 0.130); Fig. 1, right panel).
These results hold when controlling for (1) movie genre, (2) the
year the movie was released, (3) the length of the movie, (4) the
budget of the movie and (5) the arousal implied by the text as mea-
sured by the word list in Warriner et al.18. Regarding the arousal
of the text, although arousal and emotionality are related, arousal
refers to energy level, whereas the emotionality of an attitude is the
extent to which that attitude is based on emotions or feelings25,44.
Emotionality can be high or low in arousal. For example, the adjec-
tives ‘exciting’ and ‘lovable’ imply similar levels of emotionality but
higher or lower levels of arousal, respectively. Research has shown
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NAtUre HUMAN BeHAviOUr
that emotionality and arousal are separable in online reviews25.
Emotionality is thus a measure of whether a movie was able to elicit
a feeling or emotional reaction (for example, a movie as ‘inspira-
tional’, ‘enchanting’ or ‘adorable’) rather than how ‘exciting’ that
To summarize, whereas the effects of star rating were incon-
sistent across these models, emotionality was a consistent positive
predictor of box office revenue (Supplementary Table 2). Finally,
emotionality was also a significant predictor when not controlling
for any additional variables (B = 0.07; t(2,381) = 2.72; P = 0.007; 95%
CI, (0.020, 0.122); see the Supplementary Results for Study 1 for the
details of all robustness analyses).
Study 2. In Study 2, we generalized these results to a new domain.
Specifically, we predicted the success of all books on Amazon.com
from 1995 to 2015 (20 years of data). We again used the first 30
reviews for each book to index the book’s star rating (1 to 5 stars),
text valence and text emotionality. The results that follow also hold
when using an alternative cut-off (that is, the first 40 reviews) and
when using all possible reviews (see the Supplementary Results for
Study 2). We measured the success of each book on the basis of the
number of verified purchases it accrued over time.
A full 91% of the books received a positive rating by falling above
the midpoint of the star rating scale (3 stars). There were 1.6 million
positively rated books.
The regression results with average star ratings were mixed.
Aggregated ratings were a negative predictor of the number of
book purchases (B = −0.047; t(1,576,840) = 164.60; P < 0.001;
95% CI, (−0.047, −0.046)). When books rated as negative were
also included, positive star ratings were significantly predictive of
more purchases (B = 0.015; t(1,727,821) = 57.54; P < 0.001; 95% CI,
(0.015, 0.016)). However, the overall evidence here was mixed, as
star ratings were non-significant or negative predictors in 1/3 of
book genres (Supplementary Table 4).
Analysing positive books, we then predicted the book’s pur-
chases on the basis of that book’s average star rating and emotional-
ity. As in Study 1, we included text valence as a control. The average
star rating was a negative predictor of purchases (B = −0.057;
t(1,576,838) = 189.25; P < 0.001; 95% CI, (−0.058, −0.057)), and the
valence of the text was a significant positive predictor (B = 0.024;
t(1,576,838) = 78.28; P < 0.001; 95% CI, (0.024, 0.025)). Beyond
these effects, greater emotionality of the first 30 reviews predicted
greater purchases (B = 0.017; t(1,576,838) = 56.47; P < 0.001; 95%
CI, (0.016, 0.017)). Moreover, greater emotionality was predictive of
more book purchases in 93% of genres.
We also conducted robustness analyses controlling for (1) book
genre, (2) the year the book was released and (3) the arousal implied
by the review text. All primary results replicated (Supplementary
Table 5). Finally, emotionality was also a significant predic-
tor when not controlling for any additional variables (B = 0.016;
t(1,576,840) = 54.87; P < 0.001; 95% CI, (0.015, 0.016); see the
Supplementary Results for Study 2 for the details of all robustness
Study 3. Study 3 examined whether the emotionality of real-time
tweets in response to television commercials predicted success and
human behaviour in the form of daily new followers of a brand.
For both the 2016 and 2017 Super Bowls, we obtained all real-time
tweets that occurred on the day of that Super Bowl that referenced
a commercial shown during the Super Bowl. There were 94 com-
mercials across 84 businesses and a total of 187,206 tweets about
these commercials. We then used the Evaluative Lexicon to quantify
the average valence and emotionality expressed towards each com-
mercial across the tweets.
For the ratings of each commercial, we used the results from USA
Today’s Ad Meter survey, which is the most popular set of Super
Bowl ratings45. The Ad Meter survey specified to respondents that
ratings between 1 and 3 indicate a ‘poor’ commercial, between 4 and
7 a ‘good’ commercial, and between 8 and 10 an ‘excellent’ commer-
cial. Though the final number of survey participants is not disclosed
by USA Today, they indicate the panel to be in the thousands46.
We predicted the average number of daily new followers each
company obtained on Facebook in the two weeks after the Super
Bowl. This number of new followers reflects the number of individ-
uals who became interested in learning more about company and its
general offerings and took active steps to interact with that company.
Because each company has only a single Facebook page, we aggre-
gated the Twitter and ratings data at the level of each company by
averaging across that company’s commercials for each Super Bowl
(n = 84). Given that our analysis emphasized the change in new fol-
lowers that a company accrued after the Super Bowl, we controlled
for the average number of daily new followers each company gained
prior to the Super Bowl (see the Supplementary Methods for Study
3 for additional details).
The USA Today scale explicitly specifies ‘good’ commercials as
those above 3 on the scale. Thus, unlike the rating scales in Studies
1 and 2 where we counted a movie or book as positively rated if it
fell above the midpoint of the scale, using the midpoint of the USA
Today scale would not capture all of the positive commercials. We
therefore included commercials that earned a ‘good’ rating or higher
log(box office revenue)
0 5 6 7 8 9 10
Fig. 1 | Predicting movie revenue. Scatter plots, best-fit lines and 95% CIs predicting each movie’s total US box office revenue (US dollars, log transformed)
from Metacritic star ratings (left) and emotionality (right; possible range: 0 to 9). The scatter points are the raw data and thus not adjusted for covariates.
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Articles NAtUre HUMAN BeHAviOUr
(that is, above 3). In fact, 100% of commercials were rated as ‘good’
or higher across both Super Bowls. Thus, we used all observations.
We again began with a regression model that included each com-
mercial’s average USA Today rating to predict the average daily new
Facebook followers that a company gained in the two weeks after
the Super Bowl. We additionally controlled for the average daily
new Facebook followers (log transformed) that the company gained
prior to the Super Bowl to assess change. The number of followers
that a company accrued before the Super Bowl predicted the fol-
lowers they accrued after the Super Bowl (B = 0.15; t(81) = 14.57;
P < 0.001; 95% CI, (0.131, 0.171)), but the USA Today rating was
not predictive of followers (B = 0.01; t(81) = 1.39; P = 0.17; 95% CI,
We then added the average emotionality of the tweets for each
commercial as our primary predictor and the average valence as
a control. The average USA Today rating (B = 0.02; t(79) = 1.59;
P = 0.12; 95% CI, (−0.004, 0.039)) and valence of the tweets were not
predictive of the number of new followers (B = −0.02; t(79) = 1.49;
P = 0.14; 95% CI, (−0.039, 0.005)). However, beyond these effects,
the greater the emotionality of the tweets about a commercial, the
more Facebook followers a company accrued over the next two
weeks (B = 0.02; t(79) = 2.38; P = 0.02; 95% CI, (0.004, 0.042)).
Past research has indicated that the relative number of positive
versus negative tweets can be predictive of different outcomes47,48.
We therefore also included this metric as a test of the robustness
of the effects. Conceptually replicating previous research, the
greater the number of positive (minus negative) tweets a commer-
cial received, the more followers the company gained (B = 0.03;
t(78) = 2.62; P = 0.01; 95% CI, (0.007, 0.051)). As before, the USA
Today rating was not predictive (B = 0.01; t(78) = 0.53; P = 0.59; 95%
CI, (−0.016, 0.028)), and the average valence of the tweets became
a negative predictor of new followers (B = −0.02; t(78) = 2.08;
P = 0.04; 95% CI, (−0.045, −0.001)). Beyond these effects, greater
emotionality once again predicted a greater number of new follow-
ers (B = 0.02; t(78) = 2.66; P = 0.009; 95% CI, (0.007, 0.043)).
In additional robustness analyses, we controlled for (1) the num-
ber of commercials a company showed, (2) the quarter in the game
when the commercial was advertised and (3) the arousal implied
by the tweets. All effects were similar (Supplementary Table 7).
Moreover, the effects were consistent across both Super Bowls.
Emotionality was also a significant predictor when controlling only
for the average daily new Facebook followers each company gained
prior to the Super Bowl (B = 0.02; t(81) = 2.45; P = 0.016; 95% CI,
(0.005, 0.042); see the Supplementary Results for Study 3 for the
details of all robustness analyses).
Study 4. In Study 4, we examined success and human behaviour
in the form of table reservations for restaurants on the basis of the
first 30 Yelp.com reviews for all restaurants that existed in Chicago,
Illinois, as of 2017. We used these reviews to index each restaurant’s
average star rating (1 to 5 stars), text valence and text emotionality.
The results also hold when using an alternative number of reviews
(that is, the first 40 reviews) and when using all possible reviews (see
the Supplementary Results for Study 4). We examined the average
daily table reservations across a two-month period on OpenTable.
com—the most popular online table reservation service in the
United States. Across this two-month period, there were 1.30 mil-
lion table reservations (see the Supplementary Methods for Study 4
for additional details).
On Yelp, restaurants are rated on a 5-point star rating scale. As
evidence for the large number of positive reviews, 92% of restau-
rants received an average star rating that was above the midpoint of
3 stars. We used the restaurants falling above this midpoint. There
were 1,052 restaurants.
Unlike prior studies, the average star rating was predictive of
more table reservations (B = 0.05; t(1,050) = 3.06; P = 0.002; 95% CI,
(0.019, 0.085)). This outcome was the same when including even
negatively rated restaurants (B = 0.08; t(1137) = 4.97; P < 0.001;
95% CI, (0.049, 0.112); see the Supplementary Results for Study 4).
This positive predictive effect of star ratings allows us to examine
whether emotionality continues to be a unique predictor even when
ratings are initially in the positive direction.
We then added the average emotionality of the restaurant’s first
30 reviews as well as the average valence to the model. The aver-
age star rating fell to non-significance (B = −0.03; t(1,048) = 0.97;
P = 0.33; 95% CI, (−0.089, 0.030); Fig. 2, left panel), and text valence
was a positive predictor (B = 0.08; t(1,048) = 2.76; P = 0.006; 95% CI,
(0.024, 0.143)). Beyond these effects, restaurants that elicited more
emotion were associated with more table reservations (B = 0.06;
t(1,048) = 3.39; P < 0.001; 95% CI, (0.025, 0.092); Fig. 2, right panel).
We conducted additional analyses to assess the robustness of our
findings. Specifically, we controlled for (1) how well-established the
restaurant is as indexed by the relative number of years the restau-
rant has been open, (2) the neighbourhood where the restaurant is
located, (3) the cuisine of the restaurant (for example, American,
Indian or seafood), (4) the average price of a meal at the restaurant
and (5) the arousal of the text. Again, an individual can use words
that convey an emotional attitude (for example, describing a res-
taurant and its food as ‘enjoyable’, ‘comforting’ or ‘alluring’), inde-
pendent of whether it fosters high or low arousal in that individual.
We found that, across these analyses, emotionality was a significant
log(average times booked each day)
0 3 4 5
0 4.0 4.4 4.8 5.2
Fig. 2 | Predicting restaurant table reservations. Scatter plots, best-fit lines and 95% CIs predicting each restaurant’s table reservations from Yelp star
ratings (left) and emotionality (right). The scatter points are the raw data and thus not adjusted for covariates.
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NAtUre HUMAN BeHAviOUr
predictor, whereas the star rating was not (Supplementary Table 9).
Finally, emotionality was again a significant predictor when not
controlling for additional variables (B = 0.07; t(1,050) = 4.12;
P < 0.001; 95% CI, (0.036, 0.102); see the Supplementary Results for
Study 4 for the details of all robustness analyses).
Across four large-scale studies, we demonstrate that anywhere from
80% to 100% of ratings were positive. The challenge of discerning
success and how people will behave in this sea of positive ratings is
what we term the positivity problem.
Reflecting this problem, the current research indicates that mov-
ies, books, commercials and restaurants that receive similar ratings
often do not have similar levels of success. Throughout our studies,
online ratings tended to provide an unreliable signal of behaviour
towards, and thus success of, a large range of items. As one solu-
tion to this problem, we examined whether emotionality assessed
on a massive scale using computational linguistics provided a more
diagnostic signal. We found that emotionality predicted behaviour
across diverse items and several distinct sources—from Metacritic,
Amazon, Twitter, Yelp, Facebook and OpenTable.
This work has implications for work on online ratings and
discerning the aggregated wisdom from these ratings. In line
with past research, the current work further calls into question
the utility of star ratings for assessing and understanding human
behaviour and ultimately success. Research has indicated that the
predictive ability of star ratings is at best variable10–12 and at worst
not at all or even negatively predictive of behaviour and success14.
In the current work, we demonstrate similar outcomes: increas-
ingly positive ratings were commonly non-diagnostic of success.
Moreover, we demonstrate these outcomes across a wide range
of items and online platforms. As we show, one solution to this
problem is for people and organizations to pay greater attention
to the emotionality of individuals’ attitudes. One possibility is
that organizations could consider aggregating reviewers’ language
and providing an ‘emotional star rating’ to provide more mean-
ingful assessments to individuals. Future research could explore
whether star ratings can be fruitfully replaced with other, more
The aim of this research is to demonstrate the positivity problem
and the predictive ability of emotionality as one solution. As such,
one limitation to the current work is that we did not identify the
mechanism behind emotionality’s predictive ability. This research
thus provides a springboard for future work where researchers can
delve further into illuminating the paths through which emotional-
ity is able to predict human behaviour. As noted earlier, attitudes
based more on emotion tend to be stronger and more consistent
across contexts and time27,29,30,49. One reason for these outcomes is
that these attitudes tend to be stored more strongly in memory28.
Stronger links in memory predict what individuals think about and
what captures their attention in their environment, thereby pro-
viding a general guide for behaviour17,35,50. Thus, when individuals
consider which restaurant to frequent, website to visit or movie to
see again, attitudes based more on emotion are less likely to have
changed, more likely to come to mind and consequently more likely
to guide behaviour.
Additional work could explore whether attitudes based more on
emotion also affect success by increasing individuals’ propensity
to spread information via word of mouth. This may happen either
spontaneously or when individuals are directly asked for recom-
mendations. In the former case, attitudes based on emotion may
come to mind with relatively little prodding and lead individuals to
spontaneously think of and talk to others about an item. In the latter
case, when asked for a recommendation, individuals may think of
and recommend an emotion-evoking item first, given its stronger
link in memory. In line with this possibility, prior research indicates
that emotion-evoking news articles are generally more likely to be
shared with others51. Future research could explore this potential
implication of attitudes based on emotion.
We show that emotionality offers one means to solve the posi-
tivity problem, but if maximizing predictive accuracy is one’s final
goal, a second limitation of this work is that we did not maximize
predictive ability, and other solutions are possible. For example, one
approach would be to use machine learning to predict success in an
effort to maximize accuracy. However, the present approach bene-
fits from offering a theory-based solution to the positivity problem.
Indeed, machine learning is powerful in its predictive ability but
often does not provide a clear understanding of the underlying con-
structs that help provide this accuracy52. We show that emotionality,
considered of great importance across the behavioural sciences, is
predictive. In doing so, we also provide a conceptual advance to the
study of emotion itself. We show that mass-scale emotion can pre-
dict behaviour and marketplace success.
Whereas most past work on sentiment analysis has focused
on valence, the current work builds on theorizing and empiri-
cal findings in the attitudes and affective science literatures to
put forth emotionality as a unique diagnostic signal. Though the
words ‘enjoyable’ and ‘impeccable’ indicate similar levels of posi-
tivity (valence), they signal higher or lower levels of emotionality,
respectively. Through the current research, it is our hope to urge
researchers to assess factors outside of valence in the endeavour
to understand mass-scale sentiment and to use it to address issues
such as the positivity problem.
Study 1. We obtained all of the online user reviews for all movies from Metacritic.
com from 2005 to 2018 using Python v.2.7 (ref. 53) in consultation with the site
owners regarding the use of the data. We began with movies released in 2005
because this was the rst year in which there was a meaningful number of user
reviews on the platform.
We used the first 30 reviews for each movie to measure the movie’s star rating
(0 to 10 stars), text valence and text emotionality. We quantified text valence and
emotionality using the Evaluative Lexicon25. Some movies garnered fewer than 30
reviews, so we used the maximum number of reviews possible for these movies.
As a robustness analysis, we controlled for the number of initial reviews for each
movie, and the results replicate. The results also replicate when focusing only on
those movies that garnered at least 30 reviews.
We measured the success of movies using the box office revenue for each movie
(total United States box office revenue). See the Supplementary Results for Study 1
for more detail.
Study 2. We obtained all book reviews from Amazon.com from its beginning in
1995 until 2015 and used those books that had an identified genre. These reviews
are publicly available for download54,55. We used the first 30 reviews for each book
to measure the book’s star rating (1 to 5 stars), text valence and text emotionality.
We quantified text valence and emotionality using the Evaluative Lexicon.
We measured the success of each book by the number of verified purchases that
book had. See the Supplementary Results for Study 2 for more detail.
Study 3. We obtained all the tweets associated with Super Bowl commercials
from both the 2016 and 2017 Super Bowls using Python v.2.7 and in line with the
Bowl, that mentioned the name of the company or an affiliated keyword, and that
referenced either the Super Bowl or a commercial. This helped ensure that the
tweets were about the target commercials (see the Supplementary Methods for
Study 3 for additional detail).
Given that Facebook did not provide easy access to long-term historical data
for companies’ Facebook pages, we began to collect the number of followers from
each company’s Facebook page in real time as soon as that company announced
it would be advertising during the Super Bowl. This was done manually and in
most salient brand or company advertised in each commercial. As the Super
Bowl is primarily viewed by those in the United States, we used the Facebook
page specifically affiliated with the United States (for example, mercedesbenzusa)
as opposed to its worldwide Facebook page (for example, mercedesbenz). We
obtained an average of 21.85 days of daily new followers for each company
before the 2016 Super Bowl (s.d. = 7.83) and 16.05 days for the 2017 Super Bowl
(s.d. = 10.73). Capturing these pre–Super Bowl data was imperative to assess the
change in the average number of followers for each company after the Super Bowl.
NATURE HUMAN BEHAVIOUR | www.nature.com/nathumbehav
Articles NAtUre HUMAN BeHAviOUr
We then continued to extract the daily number of new followers for each
company for the two weeks after each Super Bowl. This average number of daily
new followers over these two weeks served as the dependent variable. See the
Supplementary Methods and Supplementary Results for Study 3 for more detail.
Study 4. We obtained all reviews on Yelp.com for all restaurants in Chicago,
Illinois, using Python v.2.7 in consultation with the site owners regarding the use of
the data. To do so, we used an existing database of all zip codes in the United States
and used those zip codes in the state of Illinois that directly named Chicago as the
originating city (nzip codes = 91; see the Supplementary Methods for Study 4). The
reviews began in 2004 when Yelp was first founded and continued until September
To measure the success of and behaviour towards each restaurant, we obtained
the number of daily table reservations made at all Chicago restaurants that used the
table reservation platform from OpenTable.com—the most popular online table
reservation platform in the United States56. We used Python v.2.7 and obtained
September 2017), we obtained the average number of daily table reservations
made at each restaurant. There was a total of 1.30 million table reservations
across the Chicago restaurants at this time. See the Supplementary Methods and
Supplementary Results for Study 4 for more detail.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
The data for Study 2 are available from Amazon (https://s3.amazonaws.com/
amazon-reviews-pds/readme.html). The data from Studies 1, 3 and 4 are publicly
hosted on www.metacritic.com (Study 1), www.twitter.com (Study 3), www.
facebook.com (Study 3), www.opentable.com (Study 4) and www.yelp.com (Study
4). For purposes of verification and reproducibility, readers will be provided with
the code and anonymized aggregated data results upon request. Although the
interested in the original data should contact the site administrators for permission.
The code for these analyses is available from the authors upon request.
Received: 14 May 2019; Accepted: 10 March 2021;
Published: xx xx xxxx
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We received no specific funding for this work. We thank Internet Video Archive LLC for
their assistance in providing access to the movie data and metadata from Study 1.
M.D.R., D.D.R. and L.F.N. conceptualized the work. M.D.R. obtained and analysed
the data with collaboration from D.D.R. and L.F.N. M.D.R., D.D.R. and L.F.N. wrote
The authors declare no competing interests.
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41562-021-01098-5.
Correspondence and requests for materials should be addressed to M.D.R.
Peer review information Nature Human Behaviour thanks Jonah Berger, Saif
Mohammad and the other, anonymous, reviewer(s) for their contribution to the peer
review of this work.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2021
NATURE HUMAN BEHAVIOUR | www.nature.com/nathumbehav
nature research | reporting summary April 2020
Corresponding author(s): Matthew D. Rocklage
Last updated by author(s): Feb 17, 2021
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The data for Study 2 are available from Amazon (https://s3.amazonaws.com/amazon-reviews-pds/readme.html). The data from Studies 1, 3, and 4 are publicly
hosted on www.metacritic.com (Study 1), www.twitter.com (Study 3), www.facebook.com (Study 3), www.opentable.com (Study 4), and www.yelp.com (Study 4).
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Study description Quantitative field data.
Research sample Online reviews (Metacritic, Amazon, Yelp) and tweets from Twitter. Each sample is representative of online postings from the
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we obtained all possible tweets on Twitter.
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