USER-GENERATED TWEETS ABOUT GLOBAL GREEN BRANDS: A SENTIMENT ANALYSIS APPROACH KORISNIČKI GENERIRANE TWEET PORUKE O GLOBALNIM ZELENIM MARKAMA: ...
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User-Generated Tweets About Global Green Brands: A Sentiment Analysis Approach UDK 658.626:658.89
USER-GENERATED TWEETS ABOUT
GLOBAL GREEN BRANDS: A SENTIMENT
ANALYSIS APPROACH
KORISNIČKI GENERIRANE TWEET
PORUKE O GLOBALNIM ZELENIM
MARKAMA: PRISTUP ANALIZE
SENTIMENTA
Market-Tržište
Vol. 30, No. 2, 2018, pp. 125-145
UDK 658.626:658.89
DOI http://dx.doi.org/10.22598/mt/2018.30.2.125
Original scientific paper
Saba Resnika, Mateja Kos Kokličb
a)
University of Ljubljana, Faculty of Economics, Kardeljeva ploščad 17, 1000 Ljubljana, SLOVENIA, e-mail: saba.resnik@gmail.com
b)
University of Ljubljana, Faculty of Economics, Kardeljeva ploščad 17, 1000 Ljubljana, SLOVENIA, e-mail: mateja.kos@ef.uni-lj.si
Abstract Sažetak
Purpose – Microblogging platforms are generating an in- Svrha – Platforme za mikroblogiranje generiraju bezbroj
finite volume of content on various topics. Therefore, tra- sadržaja o raznim temama. Zbog toga se tradicionalne
ditional marketing methods can hardly be employed for marketinške metode teško mogu koristiti, a nedavno
its effective research, but sentiment analysis has recently se pojavila analiza sentimenta kako bi se borila s ovim
emerged to cope with this challenge. While considerable izazovom. Unatoč znatnim akademskim naporima po-
academic effort has been devoted to investigating con- svećenim potrošačevu ponašanju usmjerenom prema
sumer behavior towards green brands, studies explicitly zelenim markama, istraživanja koja se eksplicitno bave
addressing consumer sentiments regarding such brands potrošačevim mišljenjima o tim markama i dalje su vrlo
are still rare. Hence, we apply the sentiment analysis ap- rijetka. Stoga, primjenjujemo pristup analize sentimenta
proach to investigate consumer sentiments towards 26 da bismo istražili mišljenja potrošača o 26 globalnih ze-
global green brands. lenih maraka.
Design/Methodology/Approach – First, we collected a Metodološki pristup – Prvo, prikupili smo slučajan
random set of user-generated tweets in English that were skup korisnički generiranih tweet poruka na engleskom
posted in a six-month period and included at least one jeziku koje su objavljene u razdoblju od šest mjeseci i
of the selected global green brands. When classifying the uključivale barem jednu od odabranih globalnih zelenih
Vol. 30, No. 2, 2018, pp. 125-145
posts, we extracted polarity information from a passage, maraka. Pri razvrstavanju objava izvučeni su podatci o
resulting in values ranging from positive to negative. polaritetu iz odlomaka, što je rezultiralo vrijednostima u
Findings and implications – Based on a relative frequen- rasponu od pozitivnih do negativnih.
cy word count, we found that consumers often express Rezultati i implikacije – Na temelju relativne učesta-
their sentiments about products, their characteristics and losti broja riječi utvrdili smo da potrošači često izraža-
personal consequences of using them. Next, we analyzed vaju mišljenja o proizvodima, njihovim karakteristikama
average positive and negative consumer sentiments. i osobnim posljedicama njihova korištenja. Nadalje,
As previously demonstrated, most tweets are either not analizirali smo prosječna pozitivna i negativna mišljenja
strongly affective or they are ambiguous. Based on such potrošača. Kao što je prethodno prikazano, većina tweet
125Saba Resnik, Mateja Kos Koklič
empirical insights, companies can better manage their poruka nije jako afektivna ili su dvosmislene. Na temelju
brand perception on Twitter and other social media, as takvih empirijskih uvida, poduzeća mogu bolje uprav-
an integral part of their proactive marketing strategy. ljati percepcijom svoje marke na Twitteru i drugim druš-
Limitation – The study also has some limitations. It has a tvenim medijima, što bi trebalo biti sastavni dio njihove
limited ability to reveal consumer motivations. The lex- proaktivne marketinške strategije.
icon-based method we used may sometimes fail to rec- Ograničenja – Istraživanje ima nekih ograničenja. Ima
ognize subtle forms of linguistic expression. ograničenu mogućnost otkrivanja motivacija potroša-
Originality – This research builds onto prior studies on ča. Korištena metoda bazirana na rječniku ponekad ne
green brands by applying sentiment analysis. It adds to može prepoznati suptilne oblike jezičnog izraza.
the existing knowledge by investigating consumer senti- Doprinos – Ovo istraživanje proširuje prethodna istraži-
ments towards 26 global green brands. vanja o zelenim markama primjenom analize sentimen-
Keywords – microblogging, tweets, consumer senti- ta. Dopunjuje postojeća znanja istraživanjem mišljenja
ment, sentiment analysis, global green brands potrošača o 26 globalnih zelenih maraka.
Ključne riječi – mikroblogiranje, tweet poruke, mišljenja
potrošača, analiza sentimenta, globalne zelene marke
Vol. 30, No. 2, 2018, pp. 125-145
126User-Generated Tweets About Global Green Brands: A Sentiment Analysis Approach UDK 658.626:658.89
1. INTRODUCTION be of strategic importance to companies across
various industries. Indeed, sentiment monitor-
The growing popularity of Web 2.0 is not only ing might enable companies to improve prod-
strongly aligned with consumers’ passive ab- uct quality and services, assess the impact of
sorption of online content but also with their promotional campaigns, drive sales, and identi-
creation, distribution, and exploitation of online fy new business opportunities. Another highly
content. A shift that has been noted in the past relevant outcome of measuring sentiment is
few years is the rise of user-generated content gauging users’ perceptions of companies (Hu
(in contrast to firm-created content) (Ceballos, et al., 2017; Jansen, Zhang, Sobel & Chowdury,
Crespo & Cousté, 2016). Hence, today’s chal- 2009). Given their pressing environmental and
lenge is to retrieve relevant data and transform sustainability-related concerns, companies
it into actionable information (Montoyo, Marti- have been carefully developing their identities
niz-Barco & Balahur, 2012). as “green brands”. Their aim, in doing so, is to
The exponential growth in evaluative data-rich appeal to consumers using environmental re-
resources associated with Web 2.0, such as on- sponsibility as an important element of their
line forums, web blogs, and microblogging competitive advantage (Wang, 2017).
services, has generated a huge universe of This study attempts to explore consumer sen-
content rich in public opinion on a wide array timents about major global green brands as
of subjects (Gunter, Koteyko & Atanasova, 2014). expressed via Twitter by applying sentiment
Opinions expressed on social networks as the analysis. The contribution of this study is three-
most prominent Web 2.0 platforms significantly fold. First, the most relevant contribution lies
influence public behavior across diverse areas, in exploring spontaneous expressions of con-
including the purchase of products and ser- sumer sentiment towards several global green
vices or the shaping of political views (Eirinaki, brands. Various scholars have called for a deep-
Pisal & Singh, 2012). Since the volume of such er examination of consumer perceptions of
content is infinite, especially on microblogging green brands to shed more light on the per-
services, a traditional content analysis can hardly sistently demonstrated gap between consumer
be employed for its effective research (Okazaki, perceptions of a company’s greenness and its
Diaz-Martin, Rozano & Menendez-Benito, 2014). actual sustainability performance (Cordeiro &
Consequently, several studies have recently ap- Seo, 2014; Interbrand, 2014). Hence, cognizance
plied sentiment analysis (SA) as a suitable tool of consumer attitudes and sentiments can sub-
for coping with large amounts of marketing data stantially improve the understanding of this gap.
in order to investigate brand perception, brand In addition, studies addressing consumer per-
loyalty, and brand advocacy (e.g. Hu, Bhargava, ceptions of green brands as communicated via
Fuhrmann, Ellinger & Spasojevic, 2017). Senti- microblogs have been rather sporadic, despite
ment analysis aims to examine consumer sen- the relevance of green issues (Hoepner, Dimat-
timents defined as tacit, context-specific expla- teo, Schaul, Yu & Musolesi, 2017). Second, our
nations of consumer feelings, experiences, and study provides an insight into the relationships
Vol. 30, No. 2, 2018, pp. 125-145
emotions about a product or service (Hu, Koh between brand dispersion and performance
& Reddy, 2014). The importance of consumer measures, using the contemporary approach
sentiments has been further underlined by the of sentiment analysis and a realistic user-gen-
finding that they have been recognized as early erated dataset. In particular, recent studies have
indicators of consumer attitudes – one of the indicated that brand dispersion, defined as vari-
cornerstones of consumer behavior (O’Connor, ance in brand ratings across consumers, leads
Balasubramanyan, Routledge & Smith, 2010). to reduced performance and reduced firm risk
Hence, uncovering consumer sentiments might (Luo, Raithel & Wiles, 2013). Brand dispersion re-
127Saba Resnik, Mateja Kos Koklič
flects brand polarization and can be captured of using social media opinion mining research
by applying sentiment analysis. Also, our study as a promising alternative to survey and poll-
complements previous research of Luo and oth- ing for researchers and practitioners alike. An
ers (2013) by providing a set of data obtained increasingly established category within so-
through a completely different method. Third, cial media is microblogging, which in essence
the present study is welcome due to a dearth encompasses broadcasting of brief messages
of literature that employs opinion mining tech- to some or all members of the sender’s social
niques – more specifically, sentiment analy- network through a specific web-based service
sis (e.g. Jansen et al., 2009; Mostafa, 2013). Cai, (Kaplan & Haenlein, 2011). The need to consid-
Spangler, Chen and Zhang (2010) emphasized er microblogging in today’s competitive world
the importance of focusing on sentiment mon- is substantiated by the fact that numerous mi-
itoring; they argued that the “voice of the web” croblogs mention a brand name. Interestingly,
is important for revealing consumer, brand, and 80 % of Twitter users mentioned a brand in their
market insights. tweets (Orban, Nagy, Kjarval & de Carmona,
The remainder of the paper is organized as 2014). Microblogging has evolved into a prac-
follows. First, we provide the background to tical way of sharing opinions on almost all as-
Twitter as a microblogging platform. Then, we pects of everyday life (Kontopoulos et al., 2013).
explain the concept of sentiment analysis and Consumers use microblogging to inform others
present the role of marketing research on green of what they are doing or thinking, to obtain in-
brands. Next, we describe the methodology in formation, to share information, and to forward
detail and provide exploratory and quantitative news and articles (Wood & Burkhalter, 2014). In
analyses of the gathered data. Finally, we con- the cases where a certain brand is mentioned,
clude with a discussion of the findings, limita- users might comment on products, services,
tions, and future research directions. and events held by the company, or else re-
spond to the company’s promotions (Jansen
et al., 2009). Compared to traditional blogs, mi-
2. THEORETICAL croblogs are strictly constrained in content size,
BACKGROUND but still enable users to post their opinions, ex-
periences, and queries on various topics (Kaplan
The emergence of Web 2.0 has drastically al- & Haenlein, 2011).
tered the way users perceive the Internet by im-
proving information sharing and collaboration One of the most widespread online microb-
(Kontopoulos, Berberidis, Dergiades & Bassilia- logging services is Twitter. It enables its users
des, 2013). The need to express a particular point to send and receive posts, known as “tweets”,
consisting of up to 140 characters. This charac-
of view and feelings about specific topics is in-
ter limitation results in users being more concise
herent in human nature. Social media, as one of
and eventually more expressive than via other
the cornerstones of Web 2.0, have opened up
social networks and blogs. Additionally, tweets
new possibilities for people to interact and ex-
can be processed more effectively compared
press themselves (Bravo-Marquez, Mendoza &
to lengthy blogs or articles (Kontopoulos et
Poblete, 2013).
Vol. 30, No. 2, 2018, pp. 125-145
al., 2013). Tweets might contain different forms
Social media can be described as a two-way of content, such as images, text, videos, and
communication platform that allows recipro- interactive links (Twitter, 2018). According to
cal communication between companies and Young (2010), Twitter has significantly lowered
users, and users and users (Liu & Shrum, 2002). the barriers to creating content, which is why
It has both driven and coincided with a dra- users easily share their day-to-day lives. Besides
matic change in the way of communication. the restricted length, other features of Twitter
Du and others (2015) illuminate the potential messages are the casual language style, mixed
128User-Generated Tweets About Global Green Brands: A Sentiment Analysis Approach UDK 658.626:658.89
use of symbols and words, and high frequency It can also be used to determine whether the
of grammar and spelling errors (Du et al., 2015). sentiments represent positive or negative feel-
Twitter currently records 330 million monthly ings about a specific product or service (Nasu-
active users who send over 500 million tweets kawa & Yi, 2003). In fact, the majority of studies
per day (Aslam, 2018), mostly written in the En- examine the polarity of expressed sentiments.
glish language (Mocanu et al., 2013). One of the For instance, Ortigosa, Martín and Carro (2014)
characteristics of Twitter is also a varied pool of implement this method by further refining its
authors; therefore, it is possible to collect posts protocol to extract the sentiment polarity and
of users from different social and interest groups detect significant emotional changes in Face-
(Pak & Paroubek, 2010). book messages. Previous research on senti-
In contrast to traditional consumer surveys, con- ment analysis has also included evaluations of
sumers normally post their opinions on microb- product reviews (Fang & Zhan, 2015; Kang, Yoo
logging platforms without any external trigger & Han, 2012), sentiment analysis of online news
or specification of topic (Schindler & Decker, articles and feeds (Moreo, Romero, Castro &
2013). The fact that these opinions are highly Zurita, 2012), as well as online forums and dis-
unlikely to be biased, they display a high level cussion boards (Abbasi, Chen & Salem, 2008;
of authenticity, and are affective in their nature Homburg, Ehm & Artz, 2015). In the field of poli-
makes them appealing to the majority of read- tics, researchers have used sentiment analysis to
ers. As a result, the analysis of freely expressed determine the sentiments expressed in tweets
customer opinions and related concepts, such (Hu et al., 2017; Wang, Can, Kazemzadeh, Bar &
as sentiments and evaluations, is a promising Narayanan, 2012). Additionally, researchers have
alternative to conventional survey techniques employed sentiment analysis in the field of tour-
(Decker & Trusov, 2010). ism (Alaei, Becken & Stantic, 2017; González-Ro-
dríguez, Martínez-Torres & Toral, 2016) and for
Sentiment analysis is described as the field of differentiating between informative and emo-
study that analyzes people’s opinions, senti- tional social media content (Denecke & Nejdi,
ments, evaluations, appraisals, attitudes, and 2009).
emotions towards entities, such as products,
services, organizations, individuals, issues, As evidenced, sentiment analysis has been ap-
events, topics, and their attributes (Liu, 2012). In plied in various fields of study. In addition, since
fact, rather than answering surveys about prod- sentiment analysis measures the polarity of
ucts and services, consumers freely express brands, it can be utilized to examine brand dis-
their thoughts and emotions on social media persion – a metric with a significant impact on
(Hu et al., 2017). Sentiment analysis tasks can be firm value and stock market performance. Luo
done at several levels, as suggested by Kumar and others (2013) demonstrated that brand dis-
and Sebastian (2012): word level, phrase or sen- persion is not only consistently related to lower
tence level, document level, and feature level. abnormal returns, but is also a beneficial reduc-
Techniques for sentiment analysis can be broad- tion in idiosyncratic risk. Moreover, their study
ly categorized into two classes of approaches. showed that downward dispersion is more
At the word level, sentiment analysis methods closely associated with returns than upside dis-
Vol. 30, No. 2, 2018, pp. 125-145
fall into the following two categories: (1) dic- persion is.
tionary-based approaches (Kumar & Sebastian, Sentiment analysis on microblogs is receiving
2012) and (2) machine learning approaches more and more attention from scholars since
(Pang & Lee, 2008). it contains important information stating either
Alternatively, sentiment analysis can be de- positive or negative feelings in a very limited
scribed as a technique for identifying the ways space (Chamlertwat, Bhattarakosol, Rungkasiri &
in which sentiments are expressed in the text. Haruechaiyasak, 2012). It can be used to moni-
129Saba Resnik, Mateja Kos Koklič
tor Twitter in real time in order to detect major green consumer behavior, consumer attitudes,
events and users’ reactions to events (Benhar- and sentiments regarding green brands. As a
dus & Kalita, 2013; Hsieh, Moghbel, Fang & Cho, result, the scope of academic research has mir-
2013). Sentiment analysis research also includes rored the relevance of environmental sustain-
the impact of tweets on movie sales (Rui, Liu ability. Studies on green consumers encompass
& Whinston, 2013), public mood and emotion a wide range of topics: from the socio-demo-
analysis (Bollen, Pepe & Mao, 2011), detecting graphic and psychographic profiling of green or
irony (Reyes, Rosso & Buscaldi, 2012), specifying environmentally conscious consumers (e.g. Ake-
users’ sentiments regarding different topics, and hurst, Afonso & Martins Gonçalves, 2012; Roman,
analyzing brand-related tweets (Ghiassi, Zimbra Bostan, Manolică & Mitrica, 2015) to investigat-
& Lee, 2016; Ghiassi, Skinner & Zimbra, 2013). ing drivers of, and barriers to, green consumer
Twitter offers a unique dataset in the world of behavior (e.g. Minton, Kahle & Kim, 2015; Papista,
brand sentiment since brands receive senti- Chrysochou, Krystallis & Dimitriadis, 2017; Tan,
ment messages directly from consumers in real Johnstone & Yang, 2016). Accordingly, extant
time in a public forum. Both the targeted and research has addressed individual-level deter-
the competing brands have the opportunity to minants, such as the consumer’s personality
dissect these messages to determine potential and socio-demographic characteristics (e.g. Lu,
changes in consumer sentiment. Taking ad- Chang & Chang, 2015) as well as the attitudes to-
vantage of these messages, however, requires wards specific green products, green behavior,
researchers to deal with analyzing an immense or green marketing in general (e.g. Borin, Lind-
amount of data produced by Twitter users each sey-Mullikin & Krishnan, 2013; Olsen, Slotegraaf
day (Ghiassi, Skinner & Zimbra, 2013). & Chandukala, 2014). The existing literature also
offers insight into the role of values, beliefs, and
Despite the wide range of topics analyzed with
norms in guiding consumers’ pro-environmen-
the help of sentiment analysis, only a handful
tal intentions and behaviors (e.g. Han, 2015; Steg,
of studies have probed into sentiment analysis
Bolderdijk, Keizer & Perlaviciute, 2014).
on sustainability-related topics, as an area of
immense relevance to various stakeholders. For Consumers’ favorable attitudes towards green
example, Du and others (2015) illuminated the products and practices are often inconsistent
potential of using social media opinion-mining with their actual behavior. Hence, a substantial
research as a promising alternative to surveys body of literature on green consumer behav-
and polling for both researchers and practi- ior probes into this widely acknowledged atti-
tioners. In the last few decades, consumer con- tude-behavior or intention-behavior gap (e.g.
cern for restoring the ecological balance and Aschemann-Witzel & Niebuhr Aagaard, 2014;
a significant increase in the presence of green Biswas & Roy, 2015; Johnstone & Tan, 2015; Mos-
brands have been observed in the marketplace er, 2015). These studies address why consumers’
(Chen & Chai, 2010). Green brands have been favorable attitudes towards green products and
defined as a set of attributes and benefits re- practices are often inconsistent with their actual
lating to the reduced environmental impact behavior.
of these brands and their perception as being
Vol. 30, No. 2, 2018, pp. 125-145
Additionally, companies are investing consid-
environmentally sound (Hartmann, Apaolaza
erable efforts into associating environmental
Ibáñez & Forcada Sainz, 2005). Consumers tend
issues with brands and emphasizing the impor-
to place importance on the environmental side
tance of environmental sustainability (Rios, Mar-
of sustainability (Hanss & Böhm, 2012; Hosta &
tinez & Molina, 2008). However, consumers do
Žabkar, 2016).
not always recognize a company’s green efforts,
Given their growing importance, scholars have as demonstrated by Cordeiro and Seo (2014),
also become more interested in investigating who found a significant gap between consumer
130User-Generated Tweets About Global Green Brands: A Sentiment Analysis Approach UDK 658.626:658.89
green brand recognition and companies’ actual tage of doing so is the objectivity of the evalua-
environmental performance. Further, Interbrand tive criteria used by Interbrand, which has been
(2014) identified several global green brands well established for many years and employed in
where the brand’s environmental performance several research studies (e.g., Chehab, Liu & Xiao,
was either significantly higher or significantly 2016; Wang, 2016). The initial list of 50 brands
lower than consumer perceptions of that per- spanned 11 countries and 13 industries and was
formance. For example, Interbrand identified based on consumer perceptions as well as com-
Cisco, Nokia, and L’Oréal as brands with a better pany performance – from corporate governance
actual performance than what consumers per- to management commitment, through the
ceived. Conversely, McDonald’s, Coca-Cola, and supply chain and, ultimately, the product and/
Disney were perceived to be more sustainable or service. Although those brands were spread
than they actually were. Both cases underscore across major industries, our focus was the cate-
the importance of aligning consumer knowl- gory of fast-moving consumer goods as a cat-
edge with the company’s initiatives (Cordeiro & egory that consumers are well acquainted with
Seo, 2014). and are closely related to in their daily life. Con-
sequently, we assumed that these items would
most likely be the subject of tweeting. The initial
3. METHODOLOGY
list was reduced to 34 global green brands, in-
This study employs a sentiment analysis ap- cluding: Adidas, Apple, AXA, Canon, Cisco, Co-
proach to investigate consumer sentiments re- ca-Cola, Colgate, Danone, Dell, Disney, General
garding a selection of global green brands. In ex- Electric, Heineken, H&M, IBM, IKEA, Intel, Johnson
ploring these sentiments, we applied a five-step & Johnson, Kellogg’s, L’Oreal, McDonald’s, Micro-
procedure suggested by Rambocas and Gama soft, Nestlé, Nike, Nokia, Panasonic, Pepsi, Philips,
(2013): (1) data collection; (2) text preparation; (3) Samsung, Santander, Siemens, Sony, Starbucks,
sentiment detection; (4) sentiment classification; Xerox, and ZARA. This yielded a total of 133,178
and (5) presentation of output (Figure 1). posts mentioning the brands under scrutiny.
FIGURE 1: The process of sentiment analysis
Data Text Sentiment Sentiment Presentation
collection preparation detection classification of output
In stage 1, we collected a random set of us- In the next state, we screened the extracted
er-generated tweets in English posted between data to identify content irrelevant to the area
1 September 2014 and 10 March 2015 that in- of our study. In doing so, we excluded brands
cluded at least one of the selected green brand with less than 100 posts, namely the following:
names. More specifically, since only the content AXA, Danone, General Electric, H&M, Johnson &
Vol. 30, No. 2, 2018, pp. 125-145
expressed in words was collected for this study, Johnson, Kellogg’s, Santander, and Xerox. This
other types of content, such as images and vid- resulted in the final data set containing 133,029
eos, were not included in our analysis. In case random tweets in the English language men-
the text incorporated interactive links, they were tioning any of the 26 global green brands.
also included. In order to identify relevant green
brands, we relied on the Best Global Green When classifying posts, a researcher might fo-
Brands 2014 survey conducted by the brand cus on various aspects of opinions: polarity,
consultancy Interbrand (2014). The main advan- emotions, or strength (Hu et al., 2017). Our fo-
131Saba Resnik, Mateja Kos Koklič
cus was on the polarity with the aim to extract we used these scores to determine whether a
polarity information from a passage, resulting in single tweet post is positive, neutral, or nega-
values ranging from positive to negative. Sever- tive (Liu, 2015). More specifically, we employed
al methods can be used for identifying the ori- mathematical optimization to determine three
entation of sentiment (Miao, Li & Zeng, 2010). We segments: positive (from 0.19554 to 1.0), neutral
used the lexicon-based method that requires (from 0.19028 to 0.19553), and negative (from
a pre-defined dictionary of words, WordNet, -0.19029 to -1.0). In case the posts could not be
which is commonly used for assessing positive defined as positive or negative, that is, if they
or negative sentiments. WordNet is a large lex- were not expressing a feeling or an opinion,
ical database that contains nouns, verbs, adjec- the sentiment analysis gave a neutral evalua-
tives, and adverbs, which are grouped into sets tion (Davis & O’Flaherty, 2012). For example, in
of cognitive synonyms (synsets), each express- our study, the sentiment function returned the
ing a distinct concept. Synsets are interlinked polarity value +0.78 for the tweet post ‘Fucking
by means of conceptual-semantic and lexical love Ikea!!’ ({‘polarity’: 0.78125, ‘text’: ‘Fucking love
relations. The main relation among the words Ikea!!’); therefore, we defined this tweet as posi-
in WordNet is synonymy, where words denote tive, while defining the value -0.3 for the tweet
the same concept and are interchangeable in post ‘@WallBlume_ eww i never liked Heineken’
many contexts. WordNet labels the semantic re- ({‘polarity’: −0.3, ‘text’: ‘@WallBlume_ eww i nev-
lations among words, where the most frequent er liked Heineken’) as negative.
relation among synsets is the super-subordinate The final step in the process of analyzing sen-
relation, which links more general synsets like timents is to present the output, which is out-
{furniture, piece_of_furniture} to increasingly lined in more detail in the Analysis of the data
specific ones like {bed} and {bunkbed}. WordNet section. For the purpose of pre-testing our
also labels relations such as meronymy semantic sentiment analysis tool, we also conducted a
relation, verb synsets arranged into hierarchies, short online survey with 27 respondents who
adjectives organized in terms of antonymy, and evaluated the polarity of 63 randomly selected
relations across parts of speech (POS; cross-POS tweets as positive, neutral, or negative. Their
relations) (Fellbaum, 2005; Princeton University, assessments matched the sentiment score pro-
2010). duced by the automated tool in 71 % of cases.
This overall agreement level is relatively close to
For the purposes of this study, we used a simple
the outcome of previous studies; for example,
module pattern.en combined with WordNet.
Wilson, Wiebe, and Hoffmann (2005) found an
This combination bundles a lexicon of adjec-
82 %-match between annotators’ judgments
tives (e.g. good, bad, amazing, irritating) that
and automated judgments. We estimated that
occur frequently in product reviews, annotated
the level was sufficient to proceed with the
with scores for sentiment polarity (positive
analysis.
negative). The sentiment is determined by com-
paring tweets against the expert-defined entry
in the dictionary, making it easy to determine 4. DATA ANALYSIS
Vol. 30, No. 2, 2018, pp. 125-145
the polarity of a specific sentence. For example,
words that express a desirable feeling, such as 4.1. Exploratory data analysis
“great” or “excellence”, have a positive polarity In the first phase of our empirical analysis, we
while words that express an undesirable feel- conducted exploratory research by generating
ing, such as “bad” or “awful”, have a negative relative frequency word counts. This step pro-
polarity. Based on the adjectives it contains, the vides a researcher with insight into a particular
sentiment function returns the polarity value for topic or even predicts characteristics of the
the given sentence between -1.0 and +1.0, and topic analyzed (O’Leary, 2011; Mostafa, 2013).
132User-Generated Tweets About Global Green Brands: A Sentiment Analysis Approach UDK 658.626:658.89
Namely, the frequency of appearance of terms frequency: “movie”, “life” or “live”, “world”, “dum-
is a widely used measure of interest of a specific bo”, and “channel” (Table 1). Among the top 10
topic, as shown by Asur and Huberman (2010); words, we also identified “old”, “burton”, and
they found that the number of appearances of “tim”, corresponding to an announcement that
discussion about a single topic can be used to the American film director Tim Burton will di-
predict characteristics of the topic. Before deci- rect a live-action remake of the Disney classic
phering the frequency of individual words, we Dumbo (Fritz, 2015). Finally, we also investigated
looked at how many posts were associated with the most frequent words associated with Nike.
the selected brands. We found that the leading As anticipated, the most commonly mentioned
brand is Apple with 46,612 tweets, followed by word is “air” related to the Nike Air technology
Disney with 19,414 tweets, and Nike with 14,788 and Max Air Technology. Other product-related
tweets. Figure 2 illustrates more specifically the terms among the top mentions were “size”, “jor-
number of tweets for those brands that were dan”, “max”, “shoes”, and “retro”. Given that foot-
mentioned at least 1,000 times. wear sales are the largest source of revenue for
Nike (Iyer, 2015), this outcome is somewhat ex-
pected. In addition, the word “just” appeared in
FIGURE 2: Total number of user-generated tweets slightly less than 12 % of tweets containing the
for individual brands brand name “Nike”, presumably in most men-
tions referring to the Nike logo “Just Do It”. The
Pepsi 1006
Dell 1953
top 10 words also included the words “nikeplus”
Intel 2029 and “ran”, presumably reflecting the growing
Adidas
Canon
2159
4005
popularity of running (Scheerder, 2015).
Sony 5293
Microsoft 5309
For illustration purposes, we also provide word
Starbucks 7214 frequency data for some of the selected green
Samsung 8299
McDonald's 8701
brands. Words such as “nba” and “shoes” have
Nike 14788 the highest frequency for Adidas, while words
Disney 19414
like “table”, “furniture”, and “gift” have the high-
Apple 46612
est frequency for Ikea; words such as “served”,
0 10000 20000 30000 40000 50000
“coffee”, “awesome”, and “morning” have the
Note: * Brands mentioned fewer than 1,000 times are not highest frequency for Starbucks, and words like
displayed
“camera”, “digital”, “photography”, and “kit” have
the highest frequency for the brands Canon
and Panasonic. As expected, we also found that
Subsequently, we analyzed high-frequency competitive brands are frequently mentioned
words in tweet posts using the Hermetic Word together in a single tweet, such as Adidas and
Frequency Counter 15.52 software. If focusing Nike, Samsung and Apple, Pepsi and Coca-Cola,
on the Apple brand as that with the highest Canon and Nikon. Following the previously es-
number of tweets, we noticed that by far the tablished notion that the majority of tweets are
most commonly occurring word in the tweets neutral or ambiguous (Mostafa, 2013), we addi-
Vol. 30, No. 2, 2018, pp. 125-145
is “watch” and this might be explained by the tionally re-examined the top 25 words associat-
hype surrounding the introduction of the new ed with each of the 26 selected brands to identi-
Apple Watch to the market. Along these lines, fy strongly emotional articulations. Being aware
the words “new”, “iphone”, “macbook”, and “re- of a serious limitation of such analysis, namely
veals” also reflect consumers’ close monitoring the absence of the context (words are analyzed
of the company’s actions (Table 1). In relation to in isolation), we could only characterize a few
the second most frequently mentioned brand emotionally intense words. Associated with the
– Disney, the following words had the highest McDonald’s brand and the Pepsi brand was the
133Saba Resnik, Mateja Kos Koklič
word “fuck” (in 5.4 % and 16.9 % of tweets, re- lines the fact that the majority of the tweets fall
spectively). Pepsi was also related to the words into the neutral category by having a sentiment
“wtf” and “dicks” (in 16.8 % and 16.7 %, respec- score between -0.2 and +0.2. In our study, the
tively), while Starbucks was associated with pos- share of neutral tweets ranged from 71.56 %
itively charged words “awesome” (in 3.5 % of (ZARA) to 95.74 % (L’Oreal), indicating that a
tweets) and “love” (in 2.9 % of tweets). However, large percentage of tweets are not very affec-
in most cases the frequently mentioned words tive. This is in line with the findings of other au-
by themselves have no strong connotation. thors, for example, Lindgren (2012) and Mostafa
TABLE 1: Word frequency for tweets mentioning Apple, Disney, and Nike
APPLE DISNEY NIKE
Word Number % Word Number % Word Number %
watch 20757 44.5 movie 1557 8.0 air 4824 32.6
new 2780 6.0 life 1269 6.5 size 1805 12.2
iphone 2665 5.7 world 1244 6.4 just 1750 11.8
macbook 2193 4.7 dumbo 1052 5.4 jordan 1679 11.4
reveals 2049 4.4 channel 918 4.7 max 1678 11.3
amp 2024 4.3 old 833 4.3 shoes 1579 10.7
edition 1778 3.8 burton 783 4.0 nikeplus 1265 8.6
dubai 1613 3.5 tim 772 4.0 new 1158 7.8
read 1448 3.1 throwback 765 3.9 ran 1087 7.4
now 1416 3.0 magic(al) 723 3.7 retro 1084 7.3
Note: The column displays the percentage of word frequency with respect to the total number of posts per brand.
Interestingly, when looking at the 25 most fre- (2013), who observed that it is very common for
quently mentioned words for any of the 26 sentences in the analyzed dataset to be ambig-
brands, none of them is related to “green” or uous or neutral and thus hard to place on either
“sustainable” even though these brands were side of the continuum. As evident in Figure 3,
listed as the top global green brands. This indi- the share of positive tweets exceeded the share
cates that, in general, consumers do not reflect of negative tweets for all brands except McDon-
on the companies’ greenness when tweeting, ald’s, where the situation is reverse.
but focus more on products, their characteris-
Next, we focused on the positive and the neg-
tics, and personal consequences of using these
ative tweets to shed more light on potential
products.
asymmetry in consumer sentiments about the
selected brands. More specifically, average pos-
4.2. Overall sentiment scores
Vol. 30, No. 2, 2018, pp. 125-145
itive and average negative sentiment scores
In the second part of the study, we conducted were obtained for each brand (Figure 4). Brands
quantitative research to achieve the sentiment such as Heineken (+0.43), Starbucks (+0.40), Col-
scores. Our aim was to examine the distribution gate (+0.38), and Zara (+0.38) have the highest
of sentiment scores, which were divided into average positive score, while IBM (+0.26), Sie-
five categories: from -1 to -0.6, from -0.6 to -0.2, mens (+0.27), and Philips (+0.28) are among the
from -0.2 to +0.2, from +0.2 to +0.6, and from least positively evaluated brands. This partially
+0.6 to +1 (Figure 3). This visualization under- corresponds to the sentiment distribution in
134User-Generated Tweets About Global Green Brands: A Sentiment Analysis Approach UDK 658.626:658.89
FIGURE 3: Distribution of sentiment scores for green brands (% tweets)
Adidas 89.21%
Apple 88.32%
Canon 94.38%
Cisco 84.96%
Coca-Cola 89.04%
Colgate 90.60%
Dell 81.41%
Disney 84.80%
Heiniken 80.90%
IBM 92.34%
IKEA 78.26%
Intel 81.76%
L'Oreal 95.74%
McDonald's 76.27%
Microsoft 90.51%
Nestlé 91.07%
Nike 91.94%
Nokia 94.80%
Panasonic 91.21%
Pepsi 76.74%
Philips 92.69%
Samsung 85.14%
Siemens 95.28%
Sony 93.37%
Starbucks 77.92%
ZARA 71.56%
0% 20% 40% 60% 80% 100%
from -1 to -0.6 from -0.6 to -0.2 from -0.2 to +0.2
Figure 3, showing that the IKEA, Starbucks, and ative sentiment scores. This can be paralleled
Heineken brands received the highest percent- to the findings based on Figure 3 where Micro-
age of maximally positive tweets (with scores soft, Heineken, and McDonald’s were assigned
from +0.6 to +1). On the other hand, the follow- the highest share of very negative tweets (with
Vol. 30, No. 2, 2018, pp. 125-145
ing brands have the lowest average negative scores ranging from -1 to -0.6). Some of these
scores: Nokia (-0.55), L’Oreal (-0.52), Heineken brands, such as Colgate (+0.38 and -0.42),
(-0.44), Dell (-0.43), and Microsoft (-0.42). Siemens Heineken (+0.43 and -0.44), and Zara (+0.38 and
had no negative tweets, therefore its negative -0.41) tend to evoke strongly positive as well as
score was 0.00 (the total number of tweets was strongly negative sentiments in consumers. In
low, i.e. 127), with Panasonic (-0.20) and Co- this case, consumers seem to be more divided
ca-Cola (-0.18) also receiving relatively low neg- in their affective stance on these brands.
135Saba Resnik, Mateja Kos Koklič
FIGURE 4: Average negative and average positive sentiments toward green brands
-0.35 Adidas 0.30
-0.37 Apple 0.33
-0.34 Canon 0.34
-0,35 Cisco 0.33
-0.18 Coca-Cola 0.33
-0.42 Colgate 0.38
-0.43 Dell 0.32
-0.34 Disney 0.33
-0.44 Heineken 0.43
-0.26 IBM 0.26
-0.26 IKEA 0.38
-0.30 Intel 0.32
-0.44 Loreal 0.35
-0,30 McDonald's 0.32
-0.42 Microsoft 0.30
-0.24 Nestle 0.35
-0.29 Nike 0.29
-0.55 Nokia 0.34
-0.20 Panasonic 0.33
-0.39 Pepsi 0.33
-0.32 Philips 0.28
-0.31 Samsung 0.35
0,00 Siemens 0.27
-0.27 Sony 0.29
-0.34 Starbucks 0.40
-0.41 ZARA 0.38
-0,60 -0,50 -0,40 -0,30 -0,20 -0,10 0,00 0,00 0,20 0,40 0,60
Finally, we provide a more realistic insight into Following recent calls to measure brand disper-
the strength of the positive vs. negative scores sion as an indicator of potential brand incon-
by computing the total score per brand. When sistency (Hu et al., 2017), we applied this brand
considering the overall average sentiment metric to our dataset. Hence, we calculated the
scores, all brands achieve positive values just standard deviations of the sentiment scores of
slightly above zero, ranging from +0.1 to +0.11. users’ tweets, which were grouped according
The highest score was obtained by Philips and to their brand mention. As presented in the
ZARA (in both cases +0.11), although the relative Appendix, brands with the lowest standard de-
value of the average negative score is higher viations (SD) are Siemens (SD = 0.803), Philips
than the relative value of the average positive (SD = 0.842), Sony (0.896), IBM (SD = 0.897), and
score. This indicates that the number of posi- Canon (SD = 0.901). On the other side of the
tively charged tweets was still higher, outweigh- spectrum are brands with the highest devia-
ing those negatively charged ones. Slightly tions: McDonald’s (SD = 1.885), Starbucks (SD
Vol. 30, No. 2, 2018, pp. 125-145
lower but still noteworthy is the total score for = 1.904), Zara (SD = 1.904), Pepsi (SD = 2.007),
the IKEA and Intel brands (+0.10). In these two and Heineken (SD = 2.107). Based on our sum-
cases, the average positive scores were higher mary table (provided in the Appendix), it may
than the average negative scores. In contrast, be concluded that brands with lower dispersion
the least preferred brand according to the total or polarization (lower SD) have a higher green
score estimate is McDonald’s with +0.01 as the rank. The majority of high-dispersion (high SD)
average total score, followed by Apple and Mic- brands tend to have a negative perception gap,
rosoft both with a score of +0.03. meaning that consumers perceive these com-
136User-Generated Tweets About Global Green Brands: A Sentiment Analysis Approach UDK 658.626:658.89
panies to be more green or sustainable than When analyzing word frequency in the pool
they actually are. A few other observations can of tweets, we found that the words related to
be made on the basis of the standard deviation products and their outcomes are often among
and performance measures. When comparing the most frequently mentioned. This finding
five brands with the lowest dispersion and five is consistent with the notion of Du and others
brands with the highest dispersion, it could be (2015) that tweets about sustainability are rela-
stated that, on average, low-polarizing brands tively rare compared to those discussing “hot”
have a higher variation in stock price (difference topics. Perhaps this pertains to the inherent na-
between the highest and lowest prices during ture of consumers who respond to a stimulus (a
one year). Nonetheless, among the higher-dis- product or a brand) more strongly when it per-
persion brands, Starbucks also experienced a sonally affects them (Zaichkowsky, 1985; Celsi &
particularly high variation in stock price, both Olson, 1988). This might explain why consumers
in 2014 and 2015. High-dispersion brands all re- rarely express their opinions about companies’
corded stock price growth in 2014 and 2015, ex- green activities. Presumably, consumers tend to
cept for McDonald’s, which faced a slight drop overlook these activities because they are not
in its stock price in 2014. In contrast, the majority perceived as having a direct impact on them.
of low-dispersion brands recorded a decrease in Indeed, Zhang, Peng, Zhang, Wang and Zhu
their stock price, as well as a decrease in their (2014) maintained that personal life is the most
brand value in 2015. When comparing average popular topic in microblogging posts, account-
revenues for top and bottom five brands (in ing for 45 % of the total posts. Along these lines,
terms of dispersion), it can be noted that the Liu, Burns, and Hou (2017) found product, ser-
revenues of low-dispersion brands are almost vice, and promotions to be the dominant topics
twice as high as the revenues of high-dispersion of interest to consumers when interacting with
brands. brands via Twitter.
Along with the word frequency analysis, the
current study delved into the evaluation of sen-
5. GENERAL DISCUSSION timent scores for the 26 global green brands. As
Our core focus in this study was to analyze con- previously established in the literature (e.g. Lind-
sumer sentiments with regard to an assortment gren, 2012; Mostafa, 2013), we found that most
of 26 brands recognized by Interbrand as global opinions or tweets are not affective or are am-
green brands in 2014. We specifically focused on biguous, while a minority expressed stronger
fast-moving consumer goods companies due sentiments. Among the brands with a promi-
to consumers’ daily interaction with their prod- nent share of extra positive tweets were IKEA,
ucts. Consequently, a more diverse and abun- Starbucks, and Heineken, whereas Microsoft,
dant landscape of consumer sentiments was Heineken, and McDonald’s were the brands
expected. Indeed, the selected brands seem with a higher share of extra negative tweets.
to differentiate on various levels. For example, In general, the number of positive tweets ex-
notable differences were found in the number ceeded the number of negative tweets for all
investigated green brands, with the exception
Vol. 30, No. 2, 2018, pp. 125-145
of brand mentions, with Apple being far ahead
of McDonald’s. This underpins the findings by
of other brands, supposedly as a result of being
Jansen and others (2009) that consumers are
the most valuable company worldwide among
much more likely to express positive sentiments
other reasons. Apple was awarded this position
than negative ones.
based on its brand value, estimated as the likely
future sales that are attributable to a brand and One of the indications of brands’ past overall
a royalty rate that would be charged for the use (including green) marketing efforts are also av-
of the brand (Brand Finance, 2016). erage sentiment scores, computed both in total
137Saba Resnik, Mateja Kos Koklič
as well as for the positive and negative tweets. that the stock prices of low-dispersion (or low
Heineken, Starbucks, ZARA, and Colgate were SD) brands vary somewhat more strongly than
found to be the brands with the highest av- do the stock prices of high-dispersion brands.
erage positive score, while Nokia, L’Oreal, and
With respect to the polarity of tweets, several
Heineken exhibited the most negative senti-
important implications emerged as a result of
ment scores on average. In our case, especially
the present research. Practical implications for
Heineken appeared to be the brand with sig-
users, individuals as well as companies, include
nificantly contrasting estimates from consum-
providing an overview of a general sentiment
ers’ perspective. Looking at the overall average
towards a specific brand or product. This could
sentiments scores, we found Philips and Zara to
help users save time and effort of browsing
be the best rated, while McDonald’s was recog-
through the extensive history of all posted
nized as the brand with the lowest overall senti-
tweets while also supporting their purchase
ment score. If we vaguely interpret these scores
decisions. Furthermore, sentiment analysis met-
as the indicators of consumer attitudes, we
rics can be used to influence brand decisions,
might presume that, in general, the top ranked
such as brand offerings, frequency of brand
brands – Philips and Zara – are more positively
messaging, timing of messaging, type of brand
perceived than McDonald’s. When comparing
messaging, and brand reactions to external fac-
these brands with Interbrand’s (2014) percep-
tors. This would allow brand managers to make
tion-performance gaps, the environmental per-
better use of the Twitter service and to best in-
formance of Philips and Zara is perceived to be
fluence public perception (Ghiassi et al., 2013).
worse than it actually is, while McDonald’s envi-
Although sentiment analysis cannot replace tra-
ronmental performance is perceived to be sig-
ditional measurements of customer satisfaction,
nificantly better than it actually is. It seems that
such as customer surveys, it can offer additional
a positive perception of the company’s environ-
information about customer satisfaction. In this
mental activities does not necessarily result in a
respect, it is of particular importance to pay at-
high sentiment score.
tention not only to mean values but also to dis-
Additional insight into the perceptions of persion of consumers’ evaluations. In addition,
brands on Twitter was provided by estimating companies are increasingly affected by com-
brand dispersion. For this purpose, a calculation munication in social media since customers are
of standard deviations and their examination empowered to share product- and brand-re-
in terms of their relationship to several perfor- lated sentiments among each other and such
mance measures revealed significant variations exchange might strongly affect their purchase
across the selected brands. Lower-dispersion decision processes (Stieglitz & Dang-Xuan, 2013).
(polarization or SD) brands rank higher on the Hence, companies might use sentiment analysis
Interbrand Best Global Green Brands scale, indi- to systematically monitor user-generated con-
cating a more successful performance of those tent on Twitter. Along these lines, Wijnhoven
brands as well as their better perception in cus- and Bloemen (2014) argued that sentiment
tomers’ eyes than those of higher-dispersion analysis can deliver important insights into the
brands. This corroborates prior findings by Luo word-of-mouth (WOM) regarding products and
Vol. 30, No. 2, 2018, pp. 125-145
and others (2013) about brand dispersion being services. This study adds to the growing body
negatively correlated to company performance. of literature on eWOM by focusing on consumer
In addition, our study lends support to the neg- tweets. It also affords practical implications by
ative relationship between high dispersion and using well-known global brands and the most
company risk (Luo et al., 2013) by uncovering widely-used microblogging site – Twitter.
138User-Generated Tweets About Global Green Brands: A Sentiment Analysis Approach UDK 658.626:658.89
6. CONCLUSION, we used may sometimes fail to recognize subtle
LIMITATIONS AND forms of linguistic expression (Pang & Lee, 2008).
In addition, combining the lexicon-based meth-
DIRECTIONS FOR FUTURE od with other approaches, such as machine
RESEARCH learning, might significantly improve classifi-
This research study contributes to the existing cation performance (Dhaoui et al., 2017). Third,
body of knowledge in the field of consumer we have studied 26 global green brands from
microblogging behavior. The study employed different sectors and, although they all fall into
a novel data analysis technique to investigate the category of fast-moving consumer goods,
consumer sentiments toward a selection of 26 the respective companies differ in terms of their
global green brands. By doing so, it elucidated frequency of bringing new products to the mar-
several aspects of user-generated tweets that ket, seasonality trends, need for innovation, etc.
mention global green brands: their frequency, These factors might influence the content and
sentiment polarity and, to a limited extent, their frequency of consumers’ and companies’ own
content. tweets. Consequently, these effects should be
taken into consideration in the future studies.
While offering an interesting springboard for Fourth, another limitation is the cross-sectional
future research, it also has some limitations. analysis of data collected over a longer period
First, it has a limited ability to reveal consumer of time. Namely, the current study looked at the
motivations which cannot be readily discerned dataset collected over seven months without
through sentiment analysis. To unveil deeper parsing the data longitudinally. An interesting
drivers of consumer attitudes, intentions, and aspect for future studies would be to connect
behaviors, additional qualitative insight would potential deviations in sentiment scores over a
be required. Second, although robust in detect- period of time with companies’ activities in var-
ing basic sentiments, the lexicon-based method ious markets.
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