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Convergent Innovation in Food through
Big Data and Artificial Intelligence for
Societal-Scale Inclusive Growth
Laurette Dubé, Pan Du, Cameron McRae, Neha Sharma,
Srinivasan Jayaraman, and Jian-Yun Nie
Introduction
Inclusive innovation has been proposed as a framework
to reduce inequities that have oftentimes accompanied
wealth creation and modern development since the on-
set of the first industrial revolution (Schillo & Robinson,
2017). Inclusive economic growth has been defined as
“growth that not only creates new economic opportun-
ities, but also one that ensures equal access to the op-
portunities created for all segments of society, particu-
larly for the poor” (Ali & Son, 2007). More recently, the
OECD has called for inclusiveness in “economic growth
that creates opportunities for all segments of the popu-
lation and distributes the dividends of increased
prosperity, both monetary and non-monetary terms,
fairly across society” (Planes-Satorra & Paunov, 2017).
Yet, leading economists are reporting unprecedented
increases in inequities within and across countries
Inclusive innovation has not yet reached societal scale due to a well-entrenched divide
between wealth creation and social equity. Taking food as the initial test bed, we have
proposed the convergent innovation model to address such challenges still facing 21st
century society by bridging sectors and disciplines around an integrated goal on both
sides of the social-economic divide for innovations that target wealth creation with an
upfront consideration of its externalities. The convergent innovation model is empowered
by two key enablers that integrate an advanced digital infrastructure with leading scientific
knowledge on the drivers of human behaviour in varying contexts. This article discusses
the structure, methods, and development of an artificial intelligence platform to support
convergent innovation. Insights are gathered on consumer sentiment and behavioural
drivers through the analysis of user-generated content on social media platforms.
Empirical results show that user discussions related to marketing, consequences, and
occasions are positive. Further regression modelling finds that economic consequences
are a strong predictor of consumer global sentiment, but are also sensitive to both the
actual price and economic awareness. This finding has important implications for
inclusive growth and further emphasizes the need for affordable and accessible foods, as
well as for consumer education. Challenges and opportunities inspired by the research
results are discussed to inform the design, marketing, and delivery of convergent
innovation products and services, while also contributing to dimensions of inclusion and
economic performance for equitable health and wealth.
Collectively, we have only begun to scratch the surface
of what is the biggest potential market opportunity in
the history of commerce. Those in the private sector who
commit their companies to more inclusive capitalism
have the opportunity to prosper and share their
prosperity with those who are less fortunate.
C. K. Prahalad & Stuart L. Hart (2002)
In “The Fortune at the Bottom of the Pyramid”
“”
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around the world (Piketty, 2014) at the price of con-
straining human, social, and economic progress for all
population segments in future generations (Stiglitz,
2016). As a sign of modern economies needing to modi-
fy paths to economic development, in the advanced in-
dustrial economy of the United States, overall life
expectancy at birth has actually decreased for two years
in a row and more so for poor and other disadvantaged
population segments (Kochanek et al., 2016). Wealth is
rising unequally and it is increasingly concentrated in
fewer hands, with the benefits of innovation also shared
unequally.
Considering the role that innovation has played in eco-
nomic growth since the onset of the first industrial re-
volution (Beinhocker, 2007; Drayton & Budinich, 2010;
Dubé et al., 2014), inclusive innovation holds significant
promise for addressing social and economic inequities.
Inclusive innovation projects typically aim to improve
the welfare of lower-income and marginalized groups
by enabling their full participation in the production
and consumption of social and commercial goods, ser-
vices, or programs (Chataway et al., 2014; Pansera &
Martinez, 2017). Social entrepreneurs are bringing
sophisticated technical solutions, business acumen,
and increasing investment to address inequities in both
industrialized and developing economies (Martin & Os-
berg, 2007, 2015). Commercial firms operating on differ-
ent scales now place innovative supports for the most
vulnerable and disadvantaged groups living in the com-
munities where they operate as part of their corporate
responsibility strategies (Campbell, 2007).
Pioneered by Prahalad and Hart (2002), bottom-of-the-
pyramid and other forms of frugal and lower-cost com-
mercial innovation have penetrated resource-poor mar-
kets in emerging economies and value-conscious
markets in both developing and industrialized coun-
tries. As governments and civil society groups struggle
for greater impact and longer-term viability from social
supports that still often assume never-ending access to
governmental or philanthropic funds, innovations tar-
geted to bottom-of-the-pyramid markets have a high
potential for economic growth in emerging economies
for domestic and multinational businesses while also
addressing the needs of disadvantaged populations
(Prahalad & Hammond, 2002; Prahalad & Hart, 2002).
However, such innovations occupy a limited share of
national and global wealth-creation systems in both de-
veloping and industrialized countries, and the signific-
ance of their social and economic impact for
individuals, organizations, and society remains limited
(Dubé et al., 2012).
Constraints that still prevent the above instances of in-
clusive innovation from reaching societal scale are tied
to a structural divide between pathways of poverty alle-
viation and those of wealth creation that have emerged
from the linear and siloed features of Western-centric
development since the first industrial revolution
(Gillespie et al., 2013; Moodie et al., 2013). This divide is
between the private sector – which typically focuses on
technological innovation and economic growth that
carefully caters to targeted customer needs – and the
government and civil society sectors – which typically
use a “one size fits all” approach to ensure acceptable
conditions for health, education, and other social goods
for all. This divide creates a disconnect between, on the
one hand, the still predominantly rights-based human
development approach deployed by governments and
civil society to support the poor through social welfare
and community mobilization to reach subsistence
(Devaux et al., 2009), and, on the other hand, a pre-
cisely targeted economic focus driving wealth-creation
activities in value chains and markets as industrialized
urban societies develop (Reardon et al., 2012). Further-
more, two major negative externalities of existing part-
ners of economic growth – namely healthcare and
environmental costs – are now threatening the finan-
cial viabilities of governments in industrialized and de-
veloping countries alike. It is clear that investments and
policies in current models of inclusive innovation will
not suffice unless such “externalities” become main-
stream in all industrial innovation.
With innovation accounting for 50% to 80% of all social
and economic progress tied to modern development
(Croitoru, 2012), it is only through innovating the way
we innovate that we can go beyond what has been pos-
sible so far in simultaneously advancing paths of wealth
creation and poverty alleviation in a way that fosters
lasting human and environmental health. As informa-
tion is key to transformation in complex dynamic sys-
tems (Hammond & Dubé, 2012), the most recent
developments in artificial intelligence, big data, and di-
gital technologies can serve as key catalysts for creating
an adequate and lasting supply and demand for such
innovation across the socio-economic spectrum and
around the world. Such a catalyst requires a transforma-
tion of both our methods of innovation as well as the
current practices of a broad spectrum of stakeholders,
including consumers. In fact, individuals themselves of-
ten feel divided and conflicted between their expecta-
tions, intentions, and actual behaviours as consumers
resulting in increased consumption for immediate grati-
fication or to support long-term health goals and social
or environmental causes. With modern society experi-
Convergent Innovation in Food through Big Data and Artificial Intelligence for
Societal-Scale Inclusive Growth
L. Dubé, P. Du, C. McRae, N. Sharma, S. Jayaraman, and J.-Y. Nie
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encing the fourth industrial revolution – where informa-
tion and digital technologies are in the process of repla-
cing fuel and other physical resources as drivers of both
social and economic development – convergent innova-
tion has been proposed as a next-generation approach
to both inclusive and mainstream innovation that will
bridge the social-commercial disconnect in both con-
sumers’ minds and innovation systems to build supply
and demand for societal-scale solutions (Dubé et al.,
2012; Dubé et al., 2014).
In this article, we offer a brief review of the convergent
innovation approach that has taken food as an initial
test bed, and we report on the early stages of a research
program designed with the objectives to: i) develop the
structure and methods for an artificial intelligence digit-
al platform to support convergent innovation; and ii)
generate consumer insights on sometimes conflicting
demand drivers for convergent innovation, with a focus
on user-generated content through social media. We re-
view management research on user-generated content
to inform our social media analysis for convergent in-
novation in food, then we report on the structure and
methods used for the artificial intelligence platform.
Next, we report the results from a first empirical analys-
is of user-generated content. We conclude by discuss-
ing the challenges and possibilities presented by the
research results.
Convergent Innovation
Convergent innovation, which has been in develop-
ment for more than a decade taking the food domain as
a test bed (Figure 1), is an intersectoral translational
framework that aims to innovate the way we innovate to
address some of the most complex challenges and pos-
sibilities facing 21st Century society.
The convergent innovation framework combines tech-
nical, social, and institutional innovation and bridges
science, policy, and action through a unique blend of
digital technologies and social capital with human cre-
ativity and agency. The aim is to invent a 21st century
intersectorality to improve lives, promote equity and
health, and accelerate environmental sustainability – at
the same time and through the same pathways where
wealth is being created individually and collectively.
The multifaceted intersectorality underlying conver-
gent innovation combines: i) natural, life, social, and
engineering sciences; ii) economic systems and the lar-
ger natural and social systems within which they reside;
iii) public, private, and civil society actors in each of
these systems; and iv) the various scales at which mech-
anisms, actors, and institutions are operating.
The operational deployment of convergent innovation
at scale may be made possible at this point in time by
Convergent Innovation in Food through Big Data and Artificial Intelligence for
Societal-Scale Inclusive Growth
L. Dubé, P. Du, C. McRae, N. Sharma, S. Jayaraman, and J.-Y. Nie
Figure 1. Convergent innovation: Behavioural change and ecosystem transformation solutions
(Adapted from Dubé et al., 2014)
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Convergent Innovation in Food through Big Data and Artificial Intelligence for
Societal-Scale Inclusive Growth
L. Dubé, P. Du, C. McRae, N. Sharma, S. Jayaraman, and J.-Y. Nie
two key enablers. First, is the unique digital infrastruc-
ture that defines the 4th industrial revolution, including
recent advancements in big data, artificial intelligence,
and integrative analytics to map and bridge knowledge
and its operational interfaces with policy and action.
The second key enabler integrates cutting-edge scientif-
ic knowledge on complex drivers of human behaviour
in varying contexts and their linkages to biological and
social outcomes, accelerated by the conceptual and
methodological development in genomics, neuros-
cience, and behavioural economics. The nomination of
Richard Thaler, the father of behavioural economics, as
the 2017 Nobel Laureate of Economics is a clear signal
of the scientific significance of both rational and non-
rational processes, and the importance of contexts, in
our understanding of the drivers of real-world human
behaviour. In terms of observed changes in the drivers
of human behaviours, there are promising shifts from
short-term gratifiers – such as pleasurable experiences,
convenience, and status – towards longer-term normat-
ive considerations for oneself and society. However, dis-
crepancies often remain between what one thinks,
what one intends to do, and what one does (Dubé et al.,
2008; Lin & Chang, 2012). This makes convergent innov-
ation in food quite challenging. Creating a convergent
innovation platform (Figure 2) requires deep insights
into consumer behaviour empowered by advanced
data and computer science capabilities to characterize
individual and contextual diversity in the drivers of
food choice and behaviour, as well as the correspond-
ing characteristics of innovation, strategies, and opera-
tions.
Using Social Media and Artificial Intelligence
in Management and Innovation
In this article, we focus on user-generated content from
social media as a source of behavioural insights for con-
vergent innovation. User-generated content refers to
any forms of content, such as discussion posts, that are
created by end consumers of an online system (e.g.,
Twitter) and are publically available. The proliferation
and increasing availability of user-generated content
have revolutionized industry in a new world that blurs
the lines between the physical, digital, and biological
spheres. Applications of user-generated content extend
into a new realm of industrial innovation that takes con-
sumer needs as the entry point for innovation. User-
generated content can be used to gain meaningful in-
sights from individuals in their often time-conflicting
societal roles as consumers, patients, and citizens.
Product attributes that tailor to one of these roles are of-
ten conflicting with those that are essential for meeting
the needs for other roles. In the context of food,
motives and product characteristics that typically
please consumers (e.g., taste and convenience) often
rate poorly when considering their role as a patient or
citizen. Moreover, for food, consumer packaged goods
have typically been the focus of marketing research and
practice. Yet, consumer packaged goods are just one of
many forms food can take. Big data obtained from user-
generated content, in the context of retailing, opens im-
mense opportunities along the dimensions of insights
relating to consumers, markets, products, pur-
chase/loyalty intent, and advertising at varying time
and location-points across physical and digital chan-
nels (Bradlow et al., 2017).
Consumer emotion and experience
Innovators and marketers can enrich their understand-
ing of consumers through user-generated content by
capturing their behavioural complexity to inform the
adaptive design of more competitive and consumer-
centric value chains (Kwark et al., 2017). In this new
Figure 2. Platform for convergent innovation in food
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Convergent Innovation in Food through Big Data and Artificial Intelligence for
Societal-Scale Inclusive Growth
L. Dubé, P. Du, C. McRae, N. Sharma, S. Jayaraman, and J.-Y. Nie
“market 4.0”, information and consumer insights are
connected with physical technologies to investigate
consumer voices, opinions, and reactions to products
and services (Pang et al., 2015; Thomopoulos et al.,
2015). Thus far, consumer and marketing research on
user-generated content has utilized both supervised
and unsupervised text mining for the examination of
word co-occurrence and sentiments, extracting
product characteristics, quality dimensions (e.g.,
product cost and product extension), consumer opin-
ions towards brands, and more broadly, general person-
ality characteristics of the consumer (Culotta & Cutler,
2016; Golbeck et al., 2011; Willems & Top, 2015). These
techniques allow researchers to better exploit the data
and automate processes that traditionally relied on hu-
man intervention (Lee & Bradlow, 2011). By using tech-
nology to understand such drivers of behaviour,
marketing and business strategies can be developed
that contribute to society’s journey towards sustainable
development and affordable healthcare (Dubé et al.,
2014; Hammond & Dubé, 2012) and reach the con-
sumer diet and market.
Word of mouth and recommendations
New computational methods for the analysis of user-
generated content allow researchers to dive deeply into
the understanding of affective experiences through the
dimensions of valence (i.e., attractiveness or adverse-
ness) and activation (i.e., awareness and engagement)
(De Choudhury et al., 2012). Electronic word-of-mouth
communications through social media platforms have
a significant influence on consumer behaviour (Babic
Rosario et al., 2016). High variability and large volumes
of electronic word-of-mouth communications have the
largest impact on purchasing behaviour (Babic Rosario
et al., 2016). Computational advancements in opinion
mining and sentiment analysis allow for businesses to
better understand consumer communications and re-
commendations in relation to their products and ser-
vices (Pang & Lee, 2006).
Purchase/loyalty intent
Consumer purchase intent and loyalty can also be used
by businesses to better understand their consumers.
Purchase and loyalty intent can be measured through
user-generated content (e.g., a tweet expressing a con-
sumer’s desire to purchase a product or service). Intent
may be extracted through word- or phrase-based fea-
tures, as well as through grammatical patterns (Rear-
don et al., 2014). Furthermore, user-generated content
can be classified according to the four stages of the con-
sumer decision journey (i.e., consideration, evaluation,
purchase, and post-purchase) (Vázquez et al., 2014).
Marketers and innovators can use insights gathered in
relation to purchase and loyalty intent for personalized
marketing efforts, demand planning, and market-level
sensing, as well as to inform innovation and new
product development (Reardon et al., 2014).
Market and competitive intelligence
The analysis and utility of user-generated content ex-
tend beyond understanding behaviour at the individu-
al level and allows for scalable monitoring and analysis
of broader markets, with applications for marketing in-
telligence and competitive intelligence. However, it is
important to note that user-generated content is only
one component of omnichannel retailing, and consid-
erations for a broader research perspective are needed
as consumers move through channels (physical and di-
gital) in their buying process (Verhoef et al., 2015).
User-generated content can be used to predict market
trends and outcomes (Asur & Huberman, 2010) and
gather competitor intelligence in regards to competing
companies’ products, promotions, sales, etc. from ex-
ternal sources (Dey et al., 2011). The value of user-gen-
erated content from social media sites, analyzed
through text-mining and natural language processing
technologies, are effective modalities to extract busi-
ness value and inform strategy (He et al., 2013). Togeth-
er, the monitoring of consumers, markets, and
competitors through user-generated content can be
used to inform product development and innovation
pipelines.
Product design
Inclusive innovation and convergent innovation mod
-
els can leverage technological platforms using artificial
intelligence and natural language processing to design
more consumer-centric products that benefit con
-
sumers and the broader health and economic systems.
Empirical research that explores the uses of user-gener
-
ated content from social networking sites in product de
-
velopment and innovation is sparse (Roberts & Candi,
2014). However, it is evident that user-generated con
-
tent can be used for market research to better inform
product development, engage with consumers to co-
design new products, and better collaborate in the over
-
all development process in an agile manner (Piller et al.,
2012; Roberts & Candi, 2014). Although some research
-
ers have started to use a methodology driven by data
mining to analyze user-generated content for next-gen
-
eration product design, it has yet to be applied to real-
time, population-level user-generated content for
product development or to predict consumer responses
to new products and their respective features (Goel &
Goldstein, 2013; Tuarob & Tucker, 2015).
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Convergent Innovation in Food through Big Data and Artificial Intelligence for
Societal-Scale Inclusive Growth
L. Dubé, P. Du, C. McRae, N. Sharma, S. Jayaraman, and J.-Y. Nie
Advertising
It is evident that the insights gathered through the ana-
lysis of user-generated content can be applied to many
areas of business strategy and practice. In particular,
these insights can be used by firms to market and ad-
vertise content in a precise way that resonates with con-
sumers. Marketers can tailor advertising efforts to fulfill
consumer needs for information, personal identity, and
social interaction (Knoll & Proksch, 2017). At an indi-
vidual level, user-generated content can also be lever-
aged to precisely advertise to consumers based on user
profiles and the content they post (Tucker, 2014). By
better-equipping marketers with computational tools
that meet the needs and wants of consumers, innovat-
ors can better build demand for 21st century products
and services that better bridge the divide between
health and wealth.
Building an Artificial Intelligence Platform
for Convergent Innovation in Food
To support convergent innovation, we have begun the
development of integrated modular artificial intelli-
gence platforms. The present article focuses on the so-
cial media platform that allows us to collect discussions
from social media and to extract users’ opinions and
sentiments towards different aspects of food. The over-
all architecture, as shown in Figure 3, is broadly divided
into three layers: i) the data collection and manage-
ment layer, ii) the analysis layer, and iii) the application
layer.
The functional stack covers the entire workflow for pub-
lic opinion analysis towards food. The solution can be
easily adapted to other domains with the support of cor-
responding domain knowledge. The main duty of the
first data layer is two-fold: i) to acquire domain-related
data from different social media platforms such as Twit-
ter and Facebook; and ii) to manage the ever-increasing
data to support efficient input/output operations for fu-
ture processes. The second analysis layer is a complete
text-mining workflow that also informs the construc-
tion of the food ontology and acquisition of data using
intermediate results. The final layer includes various
domain-specific applications built upon the analysis
results to support decision making.
Figure 3. Overview of the artificial intelligence platform architecture
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Convergent Innovation in Food through Big Data and Artificial Intelligence for
Societal-Scale Inclusive Growth
L. Dubé, P. Du, C. McRae, N. Sharma, S. Jayaraman, and J.-Y. Nie
Data layer
The data layer can be further divided into the functions
of seed word acquisition, data collection, and data man-
agement. Seed words are used to form search queries
submitted to the social media platforms (e.g., Twitter)
to collect data. Seed words are domain-specific and are
strongly related to the topics investigated (in our case,
food). Industry reports, iterative text analysis, and com-
mon sense were used to acquire seed words. The can-
didate words from these sources are manually filtered.
For our platform, 359 seed words were collected (includ-
ing words, phrases, and hashtags) for food.
Given the selected seed words, data is collected in three
different ways: i) searching historical data (up to 30
days) via the official application programming interface
of Twitter; ii) searching streaming data from social me-
dia (Twitter and Facebook) application programming
interfaces using the seed words; and iii) using a simu-
lated user-agent to receive new posts on social media.
Additionally, we also identify a set of known website
URLs and Facebook accounts that are related to food.
Data from the corresponding sites are collected auto-
matically.
The data acquired from different social platforms are
stored and managed in several different ways depend-
ing on the processing purpose. MongoDB is used for
real-time input/output operations. The Trec-style data
format is used for building search indices and conduct-
ing pseudo-relevance feedback searches for relevance
filtering. Json-style files are used for intermediate ana-
lyzing of results.
Analysis layer
Five types of utilities compose the analysis layer and re-
flect the processing sequences to which the digital cor-
pus is submitted – i) preprocessing, ii) feature
extraction, iii) semantic analysis, iv) taxonomy extrac-
tion, and v) aspect-ontology mapping – as described be-
low.
1. Preprocessing: The purpose of preprocessing is to fil-
ter out possible spam and to recognize the structure
of the collected raw data. To filter spam and irrelev-
ant posts, we used the Galago search engine to identi-
fy the top results using the seed words and expand
search queries based on these top results. The expan-
ded queries allow us to rank the data collected. We
consider the low-ranked data as spams. This step fil-
ters the number of posts collected down to about
60%. The other 40% of the collection is more prone to
spams and low-quality posts. Non-linguistic features
such as URLs, time, geolocation, mentions, emojis,
retweets, replies, and likes are also extracted in this
step.
2. Feature extraction: Each text goes through a series of
linguistic analysis to recognize the part-of-speech
(i.e., noun, verb, etc.) of words and the dependency
relation between words (e.g., between verb and sub-
ject), to recognize phrases that are stored in our onto-
logy and to transform (or lemmatize) a word into its
stem (e.g., “computing”, “computed”, “computes”,
and “computation” to “comput”). The connection
between the words and phrases in a discussion and
the entities stored in our ontology will allow us to
identify what aspect of food the discussion is about.
3. Semantic analysis: Our semantic analysis focuses on
sentiment analysis – to extract the sentiment (or
opinion) the user expressed about food or an aspect
of food (target) in a discussion. We use our ontology
to identify the aspect of food in the discussion, and a
sentiment dictionary (SentiWordnet) to identify senti-
ment words. Target-opinion pairs are extracted by a
classical approach based on grammatical rules: an
opinion is assumed to be related to a target if they fol-
low some grammatical pattern. For example, from
the sentence “My dessert bar was so yummy at yester-
day’s event!!!”, we can identify “dessert bar” as be-
longing to the “product” aspect, and “yummy” as a
sentiment word. The two elements are connected in
the sentence through a syntactic relation subject-pre-
dicative. Thus, they form a target-opinion pair
<dessert bar, yummy>.
4. Taxonomy extraction and 5. Aspect-ontology map-
ping: As mentioned, the ontology (or concept hier-
archy of the application) is a key component to
connect words in a sentence to food and food as-
pects. For our platform on food innovation, we per-
formed a statistical analysis on word occurrences on
all the raw data collected, with the most frequent
words manually filtered and structured into a hier-
archy.
Application layer
In the current work, the application layer was built to ex-
plore the aspects of food based on public opinion ana-
lysis. We consider five aspects of food to inform
convergent innovation: behaviour, health, con-
sequence, marketing, and characteristics. The applica-
tion layer will include several tools, but only the first
tool – aspect-based public opinion monitoring – is
presently implemented.
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Convergent Innovation in Food through Big Data and Artificial Intelligence for
Societal-Scale Inclusive Growth
L. Dubé, P. Du, C. McRae, N. Sharma, S. Jayaraman, and J.-Y. Nie
A New Approach to Semantic Analytics
This article presents a novel approach to the aggrega-
tion of population-level metadata to predict future mar-
ket trends and support the development of products
and marketing strategies. The early insights in key com-
ponents of convergent innovation in food will serve as a
springboard for articulating the formal knowledge
structure that will enable different users to interact with
the digital platforms and will define appropriate inter-
faces between diverse disciplinary and sectoral data-
sets, models, and rules (Figure 4).
The sentiment analysis (Abbasi et al., 2008; Feldman,
2013; Liu, 2012) follows the path from left to right in Fig-
ure 4. As mentioned earlier, natural language pro-
cessing techniques (Ding et al., 2015; Maas et al., 2011;
Nasukawa & Yi, 2003) such as sentence tokenization,
word tokenization, stemming, lemmatization, depend-
ency tree parsing, etc. are leveraged to acquire linguist-
ic features for extraction of aspect terms and opinion
terms as well as their relations. Rules (Liu, 2012) and
automatic text-classification approaches (Mullen & Col-
lier, 2004) were implemented for sentiment extraction.
The aspects about food are identified using our onto-
logy (Figure 5), and sentiment words are identified us-
ing external knowledge resources such as SentiWordnet
(Baccianella et al., 2010; Miller, 1995).
The first task of sentiment analysis is to determine the
polarity (positive, negative, or neutral) of a sentiment.
In SentiWordnet, each synset (a set of synonymous
Figure 4. The digital platform architecture
Figure 5. Vocabulary tree structure for convergent innovation in food
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Convergent Innovation in Food through Big Data and Artificial Intelligence for
Societal-Scale Inclusive Growth
L. Dubé, P. Du, C. McRae, N. Sharma, S. Jayaraman, and J.-Y. Nie
words) is assigned three scores: Pos, Neg, and Obj, de-
scribing their polarity distribution in general. For ex-
ample, for a synset of “yummy”, Pos(yummy)=0.75,
Neg(yummy)=0.25, Obj(yummy)=0. We simply assign
the strongest polarity to the opinion in our current im-
plementation. Thus, our earlier example of <dessert
bar, yummy> will be extended to the following triple
<dessert bar, yummy, positive>. This method can be
further extended in the future to take into account the
context (e.g., considering the target the opinion word is
describing). The triples extracted from sentences form
the set of basic opinions recognized from user discus-
sions.
The focus of the present exploratory study is to look at
consumer behaviour in relation to food using data from
social media. To this end, we use the basic opinions ex-
tracted for the analysis of sentiment scores, distribu-
tions, and influence on global sentiment (Figure 6).
Results from the analysis uncover consumer likes/dis-
likes about food aspects, as well as the drivers of beha-
viour through regression modelling.
Results
Over 26 million posts about food from Twitter and
about 1 million posts from Facebook were collected
during the summer and fall of 2017. Most posts do not
express an explicit sentiment or opinion. From this set
of data, about 70,000 target-opinion pairs were extrac-
ted. The distribution of opinions on different aspects,
and how the opinions on an aspect influence the global
sentiment of the user and post, were the subjects of our
analysis.
Sentiment probability distribution
From the extracted set of associations between the as-
pects and sentiments, we performed statistical analysis
on the distribution of sentiments across aspects of the
first- and second-order, concerning the aspects in-
cluded in our ontology. The distribution of sentiments
about the first level aspects (marketing, behaviour,
health, consequence and characteristics) is summar-
ized in Table 1a. The distribution on the sub-aspects is
shown in Table 1b. Each value in the tables represents
the joint probability. For example, P (Marketing, posit-
ive)=0.135, meaning that 13.5% of the sentiments detec-
ted are positive about marketing. In Table 1b, each
number represents the joint probability of sub-aspect
and polarity among the sentiments related to that as-
pect. For example, among all the sentiments expressed
on sub-aspects of marketing, 30.7% are positive about
the sub-aspect price specification. Notice that in Table
1b, we only count the sentiments expressed on the sub-
aspects, while ignoring those that are expressed on the
aspects directly.
Here, we observe that social media users discuss mar-
keting (P=0.135) and the consequences of food
(P=0.133) more positively than other aspects. Market-
ing-related aspects include price, promotional activit-
ies, placement, and industrial sector, and are mostly
discussed with a neutral sentiment with a slightly posit-
ive tendency. Similarly, environmental and economic
consequences are discussed with a mostly neutral-to-
positive sentiment. Food characteristics, including col-
our, texture, nutrition, packaging, and preparation
method were also discussed with a mostly positive sen-
timent. In general, social media consumers tend to talk
about food with neutral (57.6% of discussions) to posit-
ive (37.9% of discussions) sentiment. Only 4.5% of dis-
cussions included a negative sentiment towards food
overall.
The results of second-order sentiment aspects are
presented in Table 1b. Within marketing, discussions in
Figure 6. Flowchart for empirical analysis
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Convergent Innovation in Food through Big Data and Artificial Intelligence for
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L. Dubé, P. Du, C. McRae, N. Sharma, S. Jayaraman, and J.-Y. Nie
Table 1. Sentiment distribution of aspects and sub-aspects
a. Sentiment distribution on aspects
b. Sentiment distribution on sub-aspects
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Convergent Innovation in Food through Big Data and Artificial Intelligence for
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L. Dubé, P. Du, C. McRae, N. Sharma, S. Jayaraman, and J.-Y. Nie
relation to price are most probable to be neutral
(P=0.651), or positive (P=0.307) to a lesser extent, hint-
ing that consumers have a slightly positive association
with food prices on a global scale. As for the behaviour
sub-aspect, nearly all discussions are associated with
positive eating occasions (e.g., birthdays or holidays).
Consumers also have a tendency to discuss nutrition in
relation to health. Most social media posts surrounding
nutrition are positive (P=0.610) in nature (e.g., discus-
sion of high-protein content with positive sentiment).
Health benefits and wellness are less discussed by con-
sumers. The economic consequences of food (i.e., eco-
nomic impacts) are frequently discussed with a neutral
(65.6%) or positive (31.2%) sentiment, whereas the envir-
onmental impact of food is less discussed. As for food
characteristics, we evaluated aspects of packaging, taste,
texture, colour, ingredients, and product. Ingredients
and products are the most discussed aspects of food
characteristics, according our analysis. Although a large
proportion of discussions regarding ingredients are pos-
itive (42.4%), a noticeable proportion of discussion also
expresses negative opinions (14%). The results of this
analysis reveal that consumers discuss products and in-
gredients most on social media. The present analysis is
limited in terms of depth (i.e., number of levels we can
uncover below each aspect). Future studies will address
data limitations to dive deeper into acquiring more
meaningful insights (e.g., what type of texture is most
positive or negative). The insights gleaned from this ana-
lysis and future iterations will inform the development
and marketing of food products aimed at the conver-
gent innovation sweet spot illustrated in Figure 2.
Influence of each aspect on sentiment
The aspects explored in this analysis may contribute dif-
ferent degrees to the overall sentiments expressed by a
consumer expressed in a post or any unit of user-gener-
ated content, therefore impacting demand for food in
general or for specific products or contexts. To determ-
ine the influence of each aspect, we evaluated the senti-
ments of an aspect in relation to its prediction of the
global sentiment of user-generated content. We use lin-
ear regression modelling to calculate the regression
coefficients for each aspect (Kutner et al., 2004; Seber &
Lee, 2012), as shown in Table 2. The sentiment of an as-
pect valence is between -1 and 1: -1 (negative), 0 (neut-
ral), or 1 (positive). The regression formula is as follows:
where Sentimentpost is the global sentiment valence of
a post; Sentimenti is the sentiment valence of aspecti ; i
is its coefficient, which reflects the importance of the as-
pect for the global sentiment; S0 is a constant which
captures the general trend of sentiment in tweets, inde-
pendently from the aspects.
To perform the regression analysis, we have to detect
the global sentiment of a post. Therefore, a trained clas-
sifier was used to analyze the social media data. Apply-
ing this classifier, each post was automatically assigned
a sentiment valence between -1 and 1.
The regression task aims to reproduce the global senti-
ment polarities using the sentiments about food aspects
observed in the post. Table 2 shows the coefficients ob-
tained hierarchically in the linear regressions. In Table
2a, general sentiment predictions are made from the
sentiments of the first-level aspects. In Table 2b, the
sentiment of the aspect is predicted from those on the
second-level aspects. In Table 2c, the sentiment of sub-
aspects level 2 is predicted from those of the level 3 (sub-
sub-aspects). Notice that when we move down to sub-
and sub-sub-aspects, we have more and more data
sparseness, where fewer sentiments are expressed on
lower-level aspects. Therefore, the analysis cannot be
done at a very deep level with the data collected.
The global sentiment results infer that the overall senti-
ment of the food is strongly correlated with marketing
( =0.032, p<0.01), behaviour ( =0.203, p<0.001), con-
sequences ( =0.031, p<0.01), and characteristics
( =0.069, p<0.001) sub-aspects. Health is not found to
be a significant predictor of food sentiment.
Further analysis was conducted on how the sentiments
of second-order aspects influence that of an aspect
(Table 2b). Again, a linear regression was performed us-
ing a sub-corpus for each of the aspects, where the
tweets in the sub-corpus only contain tweets relating to
the aspect and its sub-aspects. Most sub-aspects in level
2 were found not to be significant. Within marketing,
price specification ( =0.288, p<0.10) and promotional
activities ( =0.051, p<0.10) are significant predictors of
positive global food sentiments, with the price having
the strongest effect. Relatedly, the economic con-
sequences ( =0.034, p<0.10) of food are also significant
predictors of global sentiment. Sub-aspects found in the
layers under behaviour, health, and characteristics were
non-significant predictors of global sentiment.
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L. Dubé, P. Du, C. McRae, N. Sharma, S. Jayaraman, and J.-Y. Nie
Price specification and economic consequences may
be further decomposed with a third level. Although
most sub-sub-aspects in this level were non-signific-
ant, price ( =0.034, p<0.10) and promotional aware-
ness ( =0.278, p<0.10) were significant predictors of
positive global sentiments, with promotional aware-
ness having the largest effect. Cost ( =0.035, p<0.10)
was also a significant predictor of positive sentiment
associated with price specification.
The above analysis constrains sub-aspects to impact
global sentiment hierarchically, meaning that each
sub-aspect is the predictor of the sentiment of the im-
mediate superior sub/aspect, with ultimately the five
first-level aspects (marketing, behaviour, health,
consequences, and characteristics) being predictors of
the global sentiments. To capture more of the richness
of user generated content, we conducted a comple-
mentary analysis where all level 2 sub-aspects were
used as direct predictors of global sentiments. Predict-
ors of a global positive sentiment emerged as culture
( =0.205, p<0.001), emotion ( =0.031, p<0.05), and per-
ception ( =0.033, p<0.01). Related to the characteristics
of food products themselves, colour ( =0.165, p<0.05),
packaging ( =0.100, p<0.10), taste ( =0.424, p<0.001),
texture ( =0.126, p<0.001), and ingredients ( =0.044,
p<0.05) were significant predictors of sentiment. Taste
had the largest effect. Nutrition ( =0.083, p<0.05), un-
der health, was also significant.
Table 2. Hierarchical estimation of contribution of aspects and sub-aspects to global sentiment
a. Level 1 coefficients b. Level 2 coefficients c. Level 3 Aspects coefficients
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Societal-Scale Inclusive Growth
L. Dubé, P. Du, C. McRae, N. Sharma, S. Jayaraman, and J.-Y. Nie
Conclusion and Future Research
Results show that positive and negative drivers of de-
mands for convergent innovation in food, as expressed
in this digital social media corpus, bear on their own be-
lief systems, experiences, and culture, as well as the
characteristics of the food they associate with and the
expected consequences that motivate their behaviour.
Environmental concerns did not emerge as salient and
significant to consumer sentiments in this corpus,
which may be tied to the present ontological structure
as well as to the sample composition. Further research
will explore this issue. In addition, although we report
results from estimation for the overall user-generated
content corpus, similar analyses could be performed
for sub-samples formed on the basis of consumer seg-
ments, type of food, competitive products, geographic-
al markets, etc.
From the perspective of inclusive innovation being con-
cerned primarily with economic equity issues, the res-
ults presented in Table 2 revealed not only that
economic consequence is a strong predictor of con-
sumer global sentiment but that it is also sensitive to
both the actual price and economic awareness. This un-
derscores the importance of the complementary
strategy to not only make food accessible at the appro-
priate price but also to inform and educate consumers
of the value and ways to estimate price as it may be re-
lated to nutrition and other dimensions of what they
need. This may be particularly relevant for disadvant-
aged population segments that are typically the target
of inclusive innovation efforts.
An important limitation to the early-state results
presented in this article is that, despite attempts to col-
lect sufficient data, we have faced data sparseness prob-
lems that may lead to counterintuitive conclusions. The
current corpus supports the analysis of the first-order
aspects in the ontology hierarchy. However, to under-
stand the lower-level concepts (aspects), a much larger
corpus will be needed to provide sufficient support for
each aspect, especially when the concept space keeps
expanding. As a limitation of the current study, further
exploration of the aspects of behaviour and health have
been excluded and will be considered in future work.
In spite of these limitations, the results of the present
study reflect the rich diversity of positive and negative
drivers of consumer demand for food products cover-
ing the full spectrum from expected consequences to
cultural, social, and emotional features of the experi-
ence, to characteristics of the consumption occasion, to
actual product design features and the marketing mix.
Although this first extraction and analysis do not allow
us to capture the set of relationships between these in-
fluential factors, future work will combine relevant the-
oretical and empirical basis with deep learning and
other artificial intelligence methods to trace the path-
ways through which a vibrant ecosystem can be created
that supports the supply and demand of a portfolio of
food that people need, want, and are willing and able to
pay for, and that the ecosystem actors are able and will-
ing to produce. These results add to the existing under-
standing of consumer behaviour for food most often
theorized from an unhealthy/tasty bipolar view for
both advantaged and disadvantaged populations, and
provides insights on the complex systems of beliefs,
motives, and goals encompassing familial and social
bonds and norms, cultural meanings, and other consid-
erations impacting consumer responses to food innova-
tion or communication. Our results provide insights to
Table 3. Estimation of contribution of level 2 sub-as-
pect to global sentiment
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About the Authors
Laurette Dubé is a Full Professor and holds the James
McGill Chair of Consumer and Lifestyle Psychology
and Marketing at the Desautels Faculty of Manage
-
ment of McGill University in Montreal, Canada. Her
research interest bears on the study of affects and be
-
havioural economic processes underlying consump
-
tion and lifestyle behaviour and how such knowledge
can inspire more effective health and marketing com
-
munications in both real life and technology-suppor
-
ted media. She is the Founding Chair and Scientific
Director of the McGill Centre for the Convergence of
Health and Economics (MCCHE). The MCCHE was
created to foster partnerships among scientists and
decision makers from all sectors of society to encour
-
age a more ambitious notion of what can be done for
more effective health management and novel path
-
ways for social and business innovation.
Pan Du is a Research Associate in the Department of
Computer Science and Operational Research at the
Université de Montréal, Canada. Before that, Pan was
an Assistant Professor at the Chinese Academy of Sci
-
ences. He received his PhD from the Institute of Com
-
puting Technology of the Chinese Academy of
Sciences. His research interests lie in text mining, in
-
formation retrieval, machine learning, and social net
-
work analysis. He has published academic papers in
various conferences and journals. He is a recipient of
the 2016 “Science and Technology Progress Award” of
the Chinese Institute of Electronics for his contribu
-
tion to a web-scale text mining system.
Cameron McRae is a Senior Research Analyst at the
McGill Centre for the Convergence of Health and Eco
-
nomics in Montreal, Canada. Since joining the centre
in 2014, he has led many translational research pro
-
jects to support innovation in the agricultural, food,
and health sectors. Cameron has strong interdisciplin
-
ary training at the nexus of science, technology, and
management, with a Bachelor of Science in Pharmaco
-
logy from McGill University, a Graduate Certificate in
Business Administration from John Molson School of
Business, and a Master of Health Informatics from the
University of Toronto. Previously, Cameron has
worked in both the public and private sectors to sup
-
port strategy and practice in the areas of governance,
business development, and business/market intelli
-
gence related to life sciences and digital health.
Continued on next page...
Convergent Innovation in Food through Big Data and Artificial Intelligence for
Societal-Scale Inclusive Growth
L. Dubé, P. Du, C. McRae, N. Sharma, S. Jayaraman, and J.-Y. Nie
find or create a convergence path among systems of
food beliefs, motives, and goals leading to individual
healthy food behaviours that are sustainable from all
these perspectives be they biological, psychological,
cultural, economic, or environmental. In fact, research
reporting results of geographical analysis of user-gener-
ated content in the future will provide geo-referenced
information on the influence of food and food cues on
food choice and suggest possibilities of fine-grained dif-
ferentiation of consumer insights for better-targeted
convergent innovation in food.
The future scope envisioned for the integrated digital
architecture for convergent innovation in food is to
combine the social media platform with others mod-
ules enabling the dynamic integration of past and
present sectoral and intersectoral knowledge and met-
rics. We also plan to move toward predictive models
that can link complex webs of relationships involved in
specific innovation and marketing practices with their
single and collective economic, social, and environ-
mental outcomes that will benefit the firm and society.
Acknowledgements
Funding for the research programs reported in this art-
icle comes partly from an SSHRC operating grant (#435-
2014-1964), a FRQSC team grant (#2015-SE-179342),
and seed project funding from the International Devel-
opment Research Centre (#107400-006) to Laurette
Dubé. Additional support comes from the Global Pulse
Innovation Platform at the McGill Centre for the Con-
vergence of Health and Economics.
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Convergent Innovation in Food through Big Data and Artificial Intelligence for
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L. Dubé, P. Du, C. McRae, N. Sharma, S. Jayaraman, and J.-Y. Nie
Neha Sharma is currently pursuing her PhD at the De
-
partment of Bioresource Engineering at McGill Uni
-
versity in Montreal, Canada. She completed her Master’s
degree in Biochemical Engineering from Harcourt Butler
Technical University, India. The title of her Master’s re
-
search project was “Optimization of Process parameters
for Bacterial solid-state fermentation of Nattokinase to
prevent myocardial infarction”, which culminated in
principles of food processing, microbiology, and biopro
-
cessing. Her Bachelor’s degree in Biotechnology is from
IMS Engineering College, India, where she took various
courses in molecular biology, genetic engineering,
bioprocess engineering, fermentation biotechnology,
food biotechnology, and environmental biotechnology,
etc. In her final year, her Bachelor’s project was based on
the study of plant extracts and their antimicrobial prop
-
erties.
Srinivasan Jayaraman is a Research Associate/Visiting
Scholar at the Desautels Faculty of Management, at Mc
-
Gill University in Montreal, Canada. He obtained his
Bachelor’s degree in Electronics and Instrumentation En
-
gineering from Bharathidasan University, India, his
MTech degree in Biomedical Engineering from SASTRA
University in Thanjavur, India, and his doctorate from
the School of Bioscience at the Indian Institute of Tech
-
nology Madras in Chennai, India. Previously, he has held
roles at TCS Innovation Labs, the University of Nebraska
Lincoln, the New Jersey Institute of Technology, and
INRS-EMT Canada. His research interests include hu
-
man behavioural and performance modelling, ontology,
ergonomics, personalized diagnosis systems, wearable
devices, biosignal processing, and human-machine inter
-
faces. In 2011, he won the MIT-TR35 young innovator
award Indian edition and was recognized as one among
the Top 50 most impactful social innovators (global list
-
ing) by World CSR Congress & World CSR Day at 2016.
Jian-Yun Nie is a Professor in Computer Science at the
University of Montreal, Canada, and is associated with
the IVADO institute. He obtained a PhD degree from Uni
-
versité Joseph Fourier of Grenoble, France. He special
-
izes in information retrieval, natural language
processing, and artificial intelligence. He has been doing
research in these areas for 30 years and has published
many papers on these topics. He has served as general
chair and PC chair for several conferences in the area of
information retrieval. He is on the board of several inter
-
national journals, including Information Retrieval Journ
-
al. He has been an invited researcher at several
institutions (Tsinghua University, Peking University) and
companies (Microsoft Research, Baidu, and Yahoo!).
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Keywords: convergent innovation, artificial
intelligence, user-generated content, social
media, food
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