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WITH MORE THAN 4,100 properties in more than 90 countries, Accor Hospitality was facing pressure from customers, as well as from shareholders, to increase customer satisfaction and quality of service during an economic downturn. It thus turned to Synthesio, a global, multilingual social-media monitoring-andresearch company, to examine the more than 5,000 customer opinions posted each month on travel websites worldwide concerning Accor's various brands. Accor saw its main challenge as how to quickly identify customer dissatisfaction and then correct problems at their source. Synthesio created a tool to track the online reputations of 12,000 hotels, including those run by Accor and those run by its competitors. It quickly revealed a number of problems Accor guests were having; for example, room keys were being demagnetized unintentionally by their smartphones. Accor was then able to work with its room-key supplier to address the problem. Accor was also able to set up.
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Unveiling the Power of Social Media Analytics
Weiguo Fan
Virginia Tech
Michael D. Gordon
University of Michigan, Ann Arbor
Forthcoming at Communications of the ACM
With more than 4,100 properties in over 90 countries, Accor Hospitality was facing
pressure to increase customer satisfaction and quality of service in the midst of an
economic downturn. To handle the situation, it turned to Synthesio, a global, multi-lingual
social media monitoring and research company, to examine the more than 5,000 customer
opinions that are posted about Accor’s various brands each month on travel sites. Accor
saw its main challenge as being able to quickly identify customer dissatisfaction and then
correct problems at their source. Synthesio created a tool specially designed to track the
online reputation of 12,000 hotels, including both Accor’s and its competitors’. It quickly
revealed a number of problems that Accor’s customers were experiencing. For example,
room keys were being de-magnetized by customers’ smart phones. Accor was then able to
work with its room key supplier to quickly fix the problem. On top of this, Accor was able to
set up a rewards and training program that encouraged individual hotels to connect with
customers through online conversations. Within one year of hiring Synthesio, the Novotel
brand within the Accor group experienced 55% growth in positive feedback in online posts
as well as a huge decrease in the number of negative comments.
Social media analytics “is concerned with developing and evaluating informatics
tools and frameworks to collect, monitor, analyze, summarize, and visualize social media
data … to facilit[ate] conversations and interactions … to extract useful patterns and
intelligence…”[28]. The Accor example illustrates how social media analytics can help
businesses. The ubiquity of smart phones and other mobile devices, Facebook and YouTube
channels devoted to companies and products, and hashtags that make it easier to instantly
and broadly share experiences all combine to create a social media landscape that is
rapidly growing and becoming ever more part of the fabric of businesses. As the number of
users on social media sites continues to increase, so does the need for businesses to
monitor and utilize these sites to their benefit.
In the remainder of this paper, we explore how the explosion in social media
necessitates the use of social media analytics; we explain the underlying stages of the social
media analytics process; we describe the most common social media analytic techniques in
use; and we discuss the ways in which social media analytics create business value.
The Need for Social Media Analytics
In the early days of social media, PR agencies would monitor customers’ posts on a
business’s own website in an attempt to identify and manage disgruntled customers. With
the explosion in the number of social media sites and the volume of use on them, this is not
nearly enough. Consider the prevalence of social media
We present throughout the paper statistics obtained from a number of websites that closely
track such issues, including:,,,
We obtained additional statistics from social media sites themselves.
Social networking is the most popular online activity
91% of online adults use social media regularly
Facebook, YouTube, and Twitter are the first, third, and tenth most-trafficked sites
on the Internet
But even these statistics fail to provide a full account of the influence that social
media are having. Users spend more than 20% of their time online on social media sites.
Facebook alone has a worldwide market penetration rate above 12%; in North America it is
50% . These rates are growing quickly, with Facebook alone gaining 170 million new users
between the first quarter of 2011 and the first quarter of 2012, an increase of 25%. Mobile
use of Facebook is growing even faster, at a 67% annual clip.
The amount of information seen during a single day gives a more startling indication
of social media’s enormous influence. Facebook’s nearly one billion active users collectively
spend approximately 20,000 years online every single day. In the same twenty-four hour
period, YouTube has over 4 billion views, amounting to 500 years of video (spread among
800 million unique users), and 140 million active Twitter users send out more than 340
million tweets.
Importantly, these are not simply passive uses of social media. YouTube’s analysis of
its videos indicates 100 million people take some sort of “social action” every week (by
liking, disliking, commenting, etc.). These actions doubled in the span of two years.
Facebook now integrates social actions in its online ads, for instance by allowing users to
see if their friends have liked or voted on products being advertised. Similarly, hashtags
on Twitter (and now other social media platforms) have given users another quick and
easy way to express their likes, dislikes, interests, and concerns, and these present further
opportunities (or challenges) to businesses that want to stay abreast of these sentiments.
The Social Media Analytics Process
Social media analytics involves a three-stage process: capture, understand, and
present. (See Figure 1). The capture stage involves obtaining relevant social media data by
monitoring or “listening” to various social media sources, archiving relevant data and
extracting pertinent information. This process can either be done by a company itself or
through a third-party vendor. Not all data that are captured will be useful. The understand
stage selects relevant data for modeling, removes noisy, low quality data, and employs
various advanced data analytic methods to analyze the data retained and gain insights from
it. The present stage deals with displaying findings from Stage 2 in a meaningful way. Our
framework is derived from familiar, broadly applied input-process-output models, and is
consistent with the approach of Zeng et al. [28], whose monitor and analyze activities are
subsumed by our understand stage; and whose summarize and visualize activities fall under
our present stage.
There is some overlap among these stages. For instance, the understand stage
creates models that can help in the capture stage. Likewise, visual analytics support human
judgments that complement the understand stage as well as assist in the present stage.
These stages are conducted in an ongoing, iterative matter rather than strictly linearly. If
the models in the understand stage fail to uncover useful patterns, they may be improved
by capturing additional data to increase their predictive power. Similarly, if presented
results are not interesting or have low predictive power, it may be necessary to return to
the understand or capture stages to adjust the data or tune the parameters used in
analytics. A system supporting social media analytics may go through several iterations
before it becomes truly useful. Data analysts and statisticians help develop and test systems
before they are used by others.
Stage 1: Capture
For a business engaged in social media analytics, the capture stage allows it to
identify conversations on social media platforms related to its activities and interests. This
is done by collecting massive amounts of relevant data across hundreds or thousands of
social media sources using news feeds, APIs, or by crawling. The capture phase covers
popular platforms such as Facebook, Twitter, LinkedIn, YouTube, Pinterest, Google +,
Tumblr, Foursquare, etc. as well as smaller, more specialized sources such as Internet
forums, blogs and microblogs, Wikis, news sites, picture sharing sites, podcasts, and social
bookmarking sites. Enormous amounts of data are archived to meet businesses’ various
needs. To prepare a data set for the understand stage, various pre-processing steps may be
performed, including data modeling, data/record linking of data from different sources,
stemming, part of speech tagging, feature extraction, and other syntactic and semantic
operations that support analysis. Information about businesses, users, events, user
comments and feedback, and other entities are also extracted for later analytical modeling
and analysis.
The capture stage must balance the need for finding information from all quarters
(inclusivity) with focusing on sources that are most relevant and authoritative (exclusivity)
to assist in more refined understanding (stage 2).
Figure 1: Social Media Analytics Process
Stage 2: Understand
Once a business has collected the conversations related to its products and
operations, it must next assess their meaning and generate metrics useful for decision-
making. This is the understand stage. Since the capture stage gathers data from many users
and sources, a sizeable portion may be noisy and may need to be removed prior to
performing any meaningful analysis. Simple, rule-based text classifiers or more
sophisticated classifiers trained on labeled data may be used for this cleaning function.
Assessing meaning from the cleaned data can involve various statistical methods and other
techniques derived from text and data mining, natural language processing, machine
translation, and network analysis [9]. This stage provides information about users’
sentimentshow they feel about the company and its productsand their behaviors
(including the likelihood of them purchasing in response to an ad campaign, for instance).
Many useful metrics and trends about users can be produced in this stage, covering their
backgrounds, interests, concerns, and networks of relationships.
Note that the understand stage is the core of the entire social media analytics
process. The success of this stage will have significant impact on the information and
metrics that are displayed in the present stage, and thus the success of future decisions or
actions that might be taken by a firm. Depending upon the techniques being used and the
information being sought, certain analyses may be pre-processed offline while others are
computed on-the-fly using data structures optimized for anticipated, ad hoc uses. Humans
may participate directly in the understand stage when visual analytics are used to allow
them to see various types and representations of data at once or to create visual “slices”
that make patterns more apparent.
Stage 3: Present
The last stage in the social media analytics process is the present stage. The results
from different analytics will be summarized, evaluated, and shown to users in an easy to
understand format. Various visualization techniques may be used to present useful
information. One of the most commonly used interface designs is the visual dashboard,
which aggregates and displays information from various sources. Sophisticated visual
analytics go beyond simply displaying information. By supporting customized views for
different users, they help make sense of large volumes of information, including patterns
that are more apparent to people than machines. Data analysts and statisticians may add
extra support during this stage.
Key Social Media Analytic Techniques
Social media analytics is a growing area that encompasses a variety of modeling and
analytical techniques from different fields. We highlight below those that are most
instrumental in understanding, analyzing, and presenting large amounts of social media
data. These techniques can support various stages of social media analytics. Sentiment
analysis and trend analysis primarily support the understand stage. Topic modeling and
social network analysis have primarily applications in the understand stage but can support
the capture and present stages as well. Visual analytics spans the understand and the
present stages.
Opinion mining (or sentiment analysis) is the core technique behind many social media
monitoring systems and trend analysis applications.
It leverage computational linguistics,
natural language processing and other methods of text analytics to automatically extract
user sentiments or opinions from text sources at any level of granularity (words or phrases
up to entire documents). Such subjective information extracted about people, products,
services, or other entities support various tasks including predicting stock market
movements, determining market trends, analyzing product defects, and managing crises.
As Pang and Lee [15] explain, the terms opinion mining and sentiment analysis each have
various definitions. We adopt their definition, which uses both terms broadly and
interchangeably to cover the subjective, textual evaluation of items or of their features.
Relatively simple methods for sentiment analysis include word (phrase) counts (the more a
product is mentioned, the more it is assumed to be liked); “polarity lexicons” (lists of
positive and negative terms that can be counted when used, say, in a text message that
mentions a product by name) [11]; and semantic methods (which may compute lexical
“distances” between a product’s name and each of two opposing terms, such as “poor” and
“excellent” to determine sentiments about it [25]. More complicated approaches must
distinguish the sentiments about more than one item referenced in the same text item
(sentence, paragraph, text message) [10].
All told, both sophisticated and simple methods of sentiment analysis can be
effective or flawed (though most research involving texts, tweets, and other short messages
has studied simple techniques.) Though sentiment analysis is becoming more common, we
must realize that sampling biases in the data can badly skew resultseven if we might
confuse large data samples for unbiased samplesperhaps especially in situations where
satisfied customers remain silent while those with more extreme positions incessantly
voice their opinions.
Topic modeling is used to sift through large bodies of captured text to detect dominant
themes (topics). The themes uncovered can be used to provide consistent labels to explore
the text collection further or to build effective navigational interfaces. Themes revealed by
topic modeling can also be used to feed other analytical tasks such as discovering user
interests, detecting emerging topics in forums or social media postings, or summarizing
parts (or all) of a text collection. Recent advances in topic modeling also allow these
algorithms to be used with streaming data from Twitter and other continuous data feeds,
making this technique an increasingly important analytic tool.
Topic modeling uses a variety of advanced statistics and machine learning
techniques. For instance, a number of models identify “latent” topics by using the co-
occurrence frequencies of words within a single communication [14], or between topics
and communities of users [27]. Information about the position of words within messages
can also be taken into consideration [26]. Please refer to [4] for a recent survey of this area.
Social network analysis is used to analyze a social network graph to understand its
underlying structure, connections, and theoretical properties as well as to identify the
relative importance of different nodes within the network. A social network graph consists
of nodes (users) and associated relationships (depicted by edges). The relationships are
typically detected from user actions directly connecting two people (such as accepting
another user as a “friend”), though they may be inferred from indirect behaviors creating
relationships, such as voting, tagging, or commenting.
Social network analysis is used to model social network dynamics and growth
(network density, locations of new node attachments, etc.) that can help monitor business
activity. Social network analysis is the primary technique for identifying key influencers in
viral marketing campaigns on Twitter or other social media platforms. It is used to detect
sub-communities within a larger online community such as a discussion forum, allowing
for greater precision in tailoring products and marketing materials. It has strong uses in
predictive modeling, such as conducting marketing campaigns aimed at those assumed
mostly likely to buy a particular product [5].
Social network analysis uses a variety of techniques pertinent to understanding the
mathematical structure of graphs [18]. These range from simpler methods (such as
counting the number of edges a node has or computing path lengths) to more sophisticated
methods that compute eigenvectors (similar to the way Google’s PageRank algorithm does)
to determine key nodes in a network. (This can be used, for instance, in determining who a
business might look to on the basis of their expertise, reputation, etc.). The analysis of
network structure significantly predates the advent of social media, being developed
mainly to analyze static mathematical graphs. Today’s large and continually changing
network structures are demanding new technical approaches, especially when real-time
decision support is sought.
Trend analysis is used for identifying and predicting future outcomes and behaviors based
on historical data collected over time. Applications of trend analysis include forecasting the
growth of customers or sales, predicting the effectiveness of ad campaigns, staying ahead of
shifts in consumers’ sentiments, forecasting movements in the stock market, etc. Trend
analysis is based upon longstanding statistical methods such as time-series analysis or
regression analysis [1] and other more recent modeling techniques such as neural
networks [12] and support vector machines [20].
Visual analytics is “the science of analytical reasoning facilitated by interactive visual
interfaces”[23]. Initially spurred on by U.S. defense needs, visualization works across
different application areas to support synthesis, exploration, discovery, and confirmation of
insights from data that are typically voluminous and spread among different sources.
Visual analytics involves a range of activities, from data collection to data-supported
decision-making. Though many statistical methods underlie visual analytics (such as
reducing high-dimensional data to fewer, and very salient, dimensions), humans’ abilities
to perceive patterns and draw conclusions are key factors as well. Indeed, when there is a
torrent of information that must be acted upon quickly, this combination of machine- and
human-strengths is critical, both in making a decision and being able to explain and justify
it. Visual analytics shares a focus with other visualization techniques on creating
economical, intuitive displays, but unlike the classical work of Tufte [24], these displays
must support real time decision-making where the stakes can be high.
Visual analytic systems must be able to process data to reveal their hidden structure
as well as their detail. Computational methods for data reduction, displaying correlations
among disparate data sources, and allowing the user to physically manipulate data displays
all underlie visual analytics. From a more user-perceptual perspective, visual analytics can
be regarded as a collection of techniques that use graphical interfaces for presenting
summarized, heterogeneous information that helps users visually inspect and understand
the results of underlying computational processes. One of the commonly used interface
designs is a dashboard where different metrics and key performance indicators are
portrayed in a way that mimics a car’s dashboard design (see Figure 2). Typically, displays
allow a user to interactively interrogate the underlying data and perform data
transformations using sliders or other types of controls. Both crisis management and
detecting breaking events from social media chatter can greatly benefit from visual
analytics. The challenge for visual analytics is to remain responsive to, and create better
visual representations for, increasingly massive and complex data requiring speedier
interpretation and display on an ever-increasing number of devices (from handhelds to
full-wall display panels).
Figure 2: Example of Radian6 Dashboard
The Business Value of Social Media Analytics
As we have suggested in discussing various techniques that support social media
analytics, there are a variety of business uses to which they can be put. Here, we consider
those uses in more detail. We adopt a life cycle analysis framework.
Social media has changed our conversations about products and services but not the
business activities underlying them. A life cycle analysis perspective considers the life of a
product (or service) from its design through its disposal, as well as support activities that
take place in parallel with these activities. Though different authors describe the product
life cycle with different levels of granularity, one that is quite typical suffices for our
purposes, having these four stages: design-development; production; utilization; and
disposal [2]. Social media is most relevant to the design-development and utilization
stages, both of which we emphasize in our discussion. In addition, we comment as well on
how social media analytics help firms gather competitive intelligence, i.e., help firms
understand more completely their business environment, suppliers, and competitors. Our
use of a life cycle framework is consistent with other social media analyses [5].
Product design-development
The first life cycle stage, product design-development, covers the conceptual,
preliminary, and detailed design of a product. Various risks threaten success during this
stage [3]: Risks involving technology change may arise from misjudging the gaps in
technology among different products or from time-to-market pressures. Design risks may
arise from a poor selection of product features, from an improper differentiation with other
products, a design's lack of modularity, or a reliance on the wrong parts.
Trend analysis and other social media analytic tools can help bring to the fore any
changes in tastes, behaviors, and other sentiments that can affect product design and
development. These tools can enable features to be added or adjusted, and they can help
create sufficient lead time for creating "next generation" products or even products in a
completely new category.
Social media analytics can also promote product innovation by capturing and
understanding conversations involving either of two groups. On the one hand, a business’s
most fanatic customers can reveal important insights, as Del Monte found in creating and
launching product in just six weeks. On the other hand, conversations with “average”
customers can also lead to product improvements. For instance, Dell created its IdeaStorm
website to solicit users’ ideas about improving its products and services. Dell takes these
suggestions into serious consideration, soliciting comments from others as (dis-)
confirmation, and, when warranted, making changes to its products.
The software industry has taken the lead in social media-based product testing
(thus leading to changes to software) by releasing various versions of its products and
soliciting reactions (and, in the case of open source programs, allowing user changes).
Other industries, too, are following suit. The most advanced use of social media-based
conversations is in the “co-creation” of products, where online users and businesses act as
informal partners in generating new product ideas and even entirely new product
categories [16].
Product Production
The risks during this life cycle phase involve supply chain responsiveness [3]. Social
media analytics can mitigate these risks. By being attuned to changing tastes and behaviors,
businesses can anticipate significant changes in demand and adjust accordingly, whether
by ramping up or down production. Visual analytics can be useful in pointing out
correlations, outliers, geographic patterns, or other trends that support smother
A business may use social media analytics, too, to learn that another business with
which it competes (or perhaps doesn’t) is having trouble with a supplier, which can be
useful in helping it anticipate and avoid the same problem, even though it is not yet
experiencing it. Close monitoring of social media can even help in technical-administrative
tasks. For instance, inventory management is based on forecasts and production schedules.
Social media may give advance warning when situations become less predictable, including
political tensions overseas that could disrupt the flow of metals, minerals, or other vital
supplies for manufacturing.
Product Utilization
The most common use of social media analytics is during the product utilization life
cycle stage. During this stage, there are three, key social media objectives: brand
awareness, brand engagement, and word of mouth [13]. Brand awareness introduces
customers to a brand (or product) or increases their familiarity. Brand engagement
increases users’ connection with a brand. Word of mouth encourages users’ attempts to
positively influence other users’ behavior.
Various metrics have been proposed for assessing social media effectiveness during
this stage [13]. For microblogging platforms including Twitter, simple metrics include:
number of tweets and followers (for brand awareness); number of followers and replies
(for brand engagement); and number of retweets (for word-of-mouth). Although these
metrics can provide important information, they are no substitute for more powerful
techniques that are increasing in importance in the era of social media.
For instance, influencer profiling uses social media to develop a deep understanding
of different users’ backgrounds, tastes, and buying behavior to create better customer
segmentation. Segmentation assists a business in more effectively reaching various groups,
by using these differences to create different strategies for increasing brand awareness and
engagement for each of them. Influencer profiling also assists in identifying social-
community leaders or experts, both of whose opinions are quite valuable in product
development and even consumer-supported customer service. A variety of techniques
support influencer profiling, including social network analysis, topic modeling, and visual
Brand engagement suggests that one feels a personal connection to a brand.
Psychometric constructs suggesting brand engagement include the terms “special bond,”
“identify with,” and “part of myself” [19]. To attempt to create this level of relationship,
companies create a variety of activities. Simple examples are “liking” or “commenting” on a
product website. Other activities aim to generate a deeper sense of connection, often by
enticing playful user actions. For instance, the car manufacturer Audi was the first to use a
then still-novel hashtag in its 2011 Super Bowl ad, showing partying, good looking
vampires, and concluding its commercial with the #SoLongVampires. This memorable
hashtag could be tweeted during the most-watched sporting event of the season. More
important, social media tools were then used to follow users who tweeted with this hashtag
to initiate a real-time dialogue that was one step, among many, of cultivating relationships
with potential new customers. To the benefit of Audi and its brand, by the end of the game
the hashtag had been become a trending topic on Twitter.
More broadly, social media analytics can allow a business to judge online reaction to
any ad campaign. The metrics produced can help link the campaign to subsequent sales and
thus the success of the campaign. Users’ reactions may also help in altering the campaign in
accordance with users’ likes and dislikes. Sentiment analysis, trend analysis, and network
analysis all provide useful support.
Word of mouth extends consumers engagement from interactions with products to
other consumers. Businesses hope these interactions (through retweets, reblogs, social
tagging, etc.) are positive; they are not always.
Customers’ online complaints about products and services are common, with, for
instance, nearly two-thirds of all customers already using social media for this purpose.
More than half of online users expect a response to a complaint within the same day but
fewer than one-third receive one. A majority of top 50 brands never respond even to
customer comments on their own websites, which obviously hurt their brand image and
reputations [17]. Viral spreading of user complaints through social media can significantly
affect firms.
Tools like real-time sentiment analysis and topic modeling allow a business to know
how its customers feel about its products and services and to respond quickly before
customer complaints become an online, negative torrent. Unofficial social media data were
harnessed to confirm the characteristics of the cholera outbreak in Haiti following the 2010
earthquake two weeks before the government and international aid agencies were able to
do so using more formal means [8]. Similarly, social media data provide early warnings
that, left unattended, can create impressions of a business that are hard to overcome.
A study of twenty brand marketers showed that the top 1% of a website’s audience
shares up to one-fifth of all links to the site and influences up to one-third of the actions
that other users take [22]. Social network analysis can be used to determine who these key
users are so that they remain satisfied, engaged, and ideally help a business market its
products on its own website and via word of mouth over these users’ social networks.
Product Disposal
Nearing the end of a product’s life, a consumer may face decisions about how to
dispose of it, and what to replace it with. For a number of consumers, being able to
ecologically responsibly dispose a product (possibly a computer) may influence their
overall impression of a company and its products. Thus, making this convenient and
ensuring that consumers are aware that it is convenient, is important. Social media
analytics can track and companies themselves can engage in conversations covering
disposal. Savvy companies that track these social media conversations can, of course, also
infer that disposal may be accompanied by a purchase of a replacement item and use that
in their marketing.
Competitive Intelligence
So far, our discussions of business values of social media analytics using the life
cycle framework focus primarily on the firms’ products/services and their customers.
Social media analytics also provide a business with value by helping it understand its
environment, suppliers, competitors, and overall business trendsin addition to its own
customers and products to stay competitive. We call this value as competitive intelligence.
We have discussed how social media analytics can reduce a firm’s production risks by
monitoring conversations about other firms in its ecosystem. Unlike gathering business
intelligence from other sources, obtaining information from social media about suppliers or
competitorsin all that they dois almost as easy for a business to do as monitoring its
own affairs.
There is one final way in which social media analytics can play a key role:
identifying and responding to crises. Ironically, businesses often cause these crises through
their own efforts at disseminating messages over social media.
Large organizations, including Burger King, the American Red Cross, and Chapstick
were implicated in social media messages that were ill-received (though, in the case of the
Red Cross, disseminated accidentally). The first two firms quickly acknowledged their
mistakes and took decisive actions. (The Red Cross very deftly defused a potential crisis,
first by responding with humor, and then, after identifying the uncommon hashtag in the
inadvertent message sent out from its account, using it to follow up to generate a successful
blood donation campaign.) Unlike Burger King and the Red Cross, Chapstick at first failed
to respond to consumers’ complaints at all and then removed them from its site without
responding. These actions exacerbated the bad publicity it generated from its online
Challenges and Conclusions
The social media landscape is vast and changing. Even as some social media
websites explode into use, quickly becoming every day tools (Facebook, launched in 2004;
Twitter in 2006), new platforms are joining them constantly (consider Pinterest, launched
in Summer 2011,which already has approximately 50 million users). All told, there are a
dozen sites with at least one hundred thousand registered users, and many more unique
visitorsincluding sites most of us have never heard of, like Ozone and Sina Weibo.
Even as businesses begin to realize the peril in ignoring social media content and,
conversely, the opportunity it presents, their questions reveal how much remains
unknown. A survey of 3,800 marketers indicated their top concerns [21]:
How to track social media return on investment
How to identify and engage with the most influential social media users
What tactics to use to create an effective social media strategy
Social media analytical tools are designed to address questions like these. At the
same time, social media are transforming the very nature of business. Current patterns
suggest that social media could produce an additional $940 billion in annual consumption,
especially in electronics, hardware, software, and mobile technologies [7]. As we have
suggested, social media are now supporting the “co-creation” of products, with consumers
working online with companies’ product designers [16].
As these new commercial frontiers open, technical challenges loom. The
extraordinary volume of “big data” will challenge social media analytics [6]. Language
issues add further complications as businesses begin to monitor and analyze social media
conversations around the world. These challenges may swell as social media analytics
begin to incorporate user-based location data facilitated by mobile technology and
pressures rise to process and respond to social messages in real-time.
The authors would like to thank Jonathan Woody at Virginia Tech for his data collection
support related to various part of this article. We would also like to thank the associate
editor and anonymous reviewers for their valuable comments in helping us improve the
quality of the paper.
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... Furthermore, by implementing MA, firms may routinely know what consumers are thinking about their brand. The social media mining technique is one example to understand what people are saying in social media about particular brands (e.g., Fan & Gordon, 2014;Wu et al., 2016). Through deep and continuous insight about the structure of consumer conversation, a firm's marketer may develop brand strategies to enhance desired brand association, brand image, and brand equity. ...
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Social media platforms are powerful for businesses to gain insight into how their customers feel about their company, product, or service. This chapter discusses the different types of social media analytics methods available to businesses to track their social media performance. With the help of natural language processing, businesses can understand the emotions associated with their brand and develop strategies to better serve their customers. With its insights into customer behavior and its ability to detect shifting trends in social data, social media analytics can provide a clear picture of how customers move from your social media presence to making purchases.
Social media analytics is the process of deriving meaning from social media data to make better business decisions. Social media platforms offer businesses new insights into their strategies through social media analytics. Social media data analytics involves extracting, cleansing, transforming, and loading social data for further analysis. The data can be analyzed to identify patterns and trends in social media use. This information can be used to improve user experience on social media and to target advertising and content to specific user groups. The key performance indicators (KPIs) for social media analysis depend on the organization’s goals and objectives for using social media. Several KPIs can measure the success of Social Media Analytics initiatives.
Culture is one of the reasons that encourage many tourists to travel, one of the places par excellences to show the culture and values of a people is the museum, a monument responsible for the transmission of knowledge between generations. Additionally, visiting museums can contribute economically to local populations. In this context, this chapter intends to analyze the experience lived by the visitor in one of the two most important museums on the African continent, Ghana, and Nigeria, through the analysis of the content generated in social media, which may or may not contribute to encouraging the visit of new tourists. The methodology used was text mining with the application of sentiment analysis. As a result, it was concluded that the visitors considered the experience very positive and described it as an interesting, valuable, and beautiful/wonderful visit.
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Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.
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Text mining tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, this book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, it explores current real-world, mission-critical applications of text mining and link detection in such varied fields as M&A business intelligence, genomics research and counter-terrorism activities.
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Data races are one of the most common and subtle causes of pernicious concurrency bugs. Static techniques for preventing data races are overly conservative and do not scale well to large programs. Past research has produced several dynamic data race detectors that can be applied to large programs. They are precise in the sense that they only report actual data races. However, dynamic data race detectors incur a high performance overhead, slowing down a program's execution by an order of magnitude. In this paper we present LiteRace, a very lightweight data race detector that samples and analyzes only selected portions of a program's execution. We show that it is possible to sample a multithreaded program at a low frequency, and yet, find infrequently occurring data races. We implemented LiteRace using Microsoft's Phoenix compiler. Our experiments with several Microsoft programs, Apache, and Firefox show that LiteRace is able to find more than 70% of data races by sampling less than 2% of memory accesses in a given program execution.
This article studies the problem of latent community topic analysis in text-associated graphs. With the development of social media, a lot of user-generated content is available with user networks. Along with rich information in networks, user graphs can be extended with text information associated with nodes. Topic modeling is a classic problem in text mining and it is interesting to discover the latent topics in text-associated graphs. Different from traditional topic modeling methods considering links, we incorporate community discovery into topic analysis in text-associated graphs to guarantee the topical coherence in the communities so that users in the same community are closely linked to each other and share common latent topics. We handle topic modeling and community discovery in the same framework. In our model we separate the concepts of community and topic, so one community can correspond to multiple topics and multiple communities can share the same topic. We compare different methods and perform extensive experiments on two real datasets. The results confirm our hypothesis that topics could help understand community structure, while community structure could help model topics.
Conference Paper
Probabilistic topic modeling provides a suite of tools for the unsupervised analysis of large collections of documents. Topic modeling algorithms can uncover the underlying themes of a collection and decompose its documents according to those themes. This analysis can be used for corpus exploration, document search, and a variety of prediction problems. In this tutorial, I will review the state-of-the-art in probabilistic topic models. I will describe the three components of topic modeling: (1) Topic modeling assumptions (2) Algorithms for computing with topic models (3) Applications of topic models In (1), I will describe latent Dirichlet allocation (LDA), which is one of the simplest topic models, and then describe a variety of ways that we can build on it. These include dynamic topic models, correlated topic models, supervised topic models, author-topic models, bursty topic models, Bayesian nonparametric topic models, and others. I will also discuss some of the fundamental statistical ideas that are used in building topic models, such as distributions on the simplex, hierarchical Bayesian modeling, and models of mixed-membership. In (2), I will review how we compute with topic models. I will describe approximate posterior inference for directed graphical models using both sampling and variational inference, and I will discuss the practical issues and pitfalls in developing these algorithms for topic models. Finally, I will describe some of our most recent work on building algorithms that can scale to millions of documents and documents arriving in a stream. In (3), I will discuss applications of topic models. These include applications to images, music, social networks, and other data in which we hope to uncover hidden patterns. I will describe some of our recent work on adapting topic modeling algorithms to collaborative filtering, legislative modeling, and bibliometrics without citations. Finally, I will discuss some future directions and open research problems in topic models.
Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. A SVM classifiers creates a maximum-margin hyperplane that lies in a transformed input space and splits the example classes, while maximizing the distance to the nearest cleanly split examples. The parameters of the solution hyperplane are derived from a quadratic programming optimization problem. Here, we provide several formulations, and discuss some key concepts.
This paper reports on the development of social network analysis, tracing its origins in classical sociology and its more recent formulation in social scientific and mathematical work. It is argued that the concept of social network provides a powerful model for social structure, and that a number of important formal methods of social network analysis can be discerned. Social network analysis has been used in studies of kinship structure, social mobility, science citations, contacts among members of deviant groups, corporate power, international trade exploitation, class structure, and many other areas. A review of the formal models proposed in graph theory, multidimensional scaling, and algebraic topology is followed by extended illustrations of social network analysis in the study of community structure and interlocking directorships.