Expert answer:use of online reviews
Solved by verified expert:As shown in Xiang et al. (2015), online reviews can be used as data to understand fundamental concepts such as guest satisfaction in hospitality management using text analytical tools. Obviously, hospitality management involves a diverse range of issues and problems. In this exercise, please work with your partner to propose an idea to use online product reviews to examine the market in order to generate insights about competitive intelligence for a specific hospitality business. Your response could be concise but should at least cover the following: 1. A brief description of the business; 2. What do you want to find out? 3. Where are you going to find the data and what tools are you going to use? 4. Justify why this will yield what you are looking for.you might find this link very helpfulhttps://www.tnooz.com/article/how-to-combine-socia…PS read and check out the 2 attached papers with this question as they are also very helpful.
power_of_social_media_analytics.pdf
soical_media_online_review_1.pdf
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contributed articles
DOI:10.1145/ 2602574
How to use, and influence, consumer social
communications to improve business
performance, reputation, and profit.
BY WEIGUO FAN AND MICHAEL D. GORDON
The Power of
Social Media
Analytics
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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a rewards-and-training program that
encouraged its individual hotels to
connect with their guests through online conversations. Within one year
of contracting Synthesio, the Novotel
brand within the Accor group saw 55%
growth in positive feedback in online
posts, along with a significant 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 Accor illustrates how
social media analytics can help businesses improve their reputations and
resulting business performance. Ubiquitous smartphones and other mobile
devices, Facebook and YouTube channels devoted to companies and products, and hashtags that make it easy to
share experiences instantly combine to
create a social media landscape that is
quickly becoming part of the fabric of
everyday business operations. As the
number of users on social media sites
continues to grow, so does the need for
businesses to monitor and use them to
their advantage.
Through the rest of this article, we
explore how social media popularity
necessitates use of social-media analytics, the underlying stages of the analytics process, the most common social
media analytic techniques, and the
ways analytics creates business value.
key insights
S ocial media analytics involves a threestage process: capture, understand,
and present.
K ey techniques go beyond text analytics to
include opinion mining, sentiment analysis,
topic modeling, social network analysis,
trend analysis, and visual analytics.
B usinesses can use them to realize
value in all phases of a product or
service life cycle, including insight
into changing consumer interests and
tastes, influential users, ad-campaign
effectiveness, how to respond to crises,
and competitive intelligence.
IMAGE COLL AGE F ROM SH UTTERSTOCK.COM
The Need
In the early days of social media—the
mid-2000s—PR agencies would monitor customers’ posts on a business’s
own website to try to identify and manage unhappy customers. With the explosion in the number of social media
sites and volume of users on them,
monitoring alone is not enough to render a complete picture of how a company is doing. Consider the pervasiveness
of social mediaa:
˲˲ Social networking is the most popular online activity;
˲˲ 91% of adults online are regular users of social media; and
˲˲ Facebook, YouTube, and Twitter
are the second, third, and eighth mosttrafficked sites on the Internet, as of
April 2014.
However, even these statistics do
not fully account for the influence
social media has on our lives. Users
spend more than 20% of their time
online on social media sites. Facea Throughout this article, we cite statistics from
a number of websites that closely track these issues, including http://www.adweek.com, http://
www.alexa.com,
http://www.internetworldstats.com, and http://www.comscore.com, as
well as from social media sites themselves.
book alone has a worldwide market
penetration rate over 12% of the entire
online population; 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%.
Facebook mobile use is growing even
more quickly, at a 67% annual clip, as
of Summer 2013.
The amount of information seen by
all these users on a typical day gives a
clearer indication of the enormous influence of social media. As of October
2012, Facebook’s nearly one billion
active users collectively were spending approximately 20,000 years online
each day. In the same period, YouTube
reported more than one billion views,
or 500 years of video (spread among
800 million unique users), and 140
million active Twitter users sent more
than 340 million tweets.
These are not simply passive uses.
YouTube’s analysis of its videos indicates 100 million people take some
sort of “social action” each week, by,
say, liking, disliking, or commenting on what they see. These actions
doubled from 2012 to 2013. Facebook
integrates social actions in its online
ads today by, for instance, allowing users to see if their friends have liked or
voted on products being advertised.
Likewise, hashtags on Twitter (as well
as other social-media platforms) give
users another quick and easy way to
express their likes, dislikes, interests,
and concerns, presenting further opportunities (or challenges) to businesses striving to use them.
The Process
Social media analytics involves a threestage process: “capture,” “understand,” and “present” (see Figure 1),
the CUP framework. Capture involves
obtaining relevant social media data
by monitoring, or “listening,” to various social-media sources, archiving
relevant data and extracting pertinent
information. It can be done by a company itself or through a third-party
vendor. Not all captured data is useful,
however. Understand selects relevant
data for modeling while removing
noisy low-quality data, using various
advanced data analytic methods on the
data and gain insight from them. And
present deals with displaying findings
from the understand state in a mean-
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contributed articles
ingful way. We derived the CUP framework from familiar, broadly applied
input-process-output models, making
it consistent with Zeng et al.,28 whose
monitor-and-analyze activities were
subsumed by our understand stage
and whose summarize-and-visualize
activities fall within our present stage.
There is also some overlap among
the stages; for instance, the understand
stage creates models that can help in
the capture stage. Likewise, visual analytics supports human judgments that
complement the understand stage, as
well as assist in the present stage. The
stages are conducted in an ongoing, iterative manner rather than strictly linearly. If the models in the understand
stage fail to reveal useful patterns, they
may be improved by capturing additional data to increase their predictive
power. Likewise, if presented results
are not interesting or lack predictive
power, it may be necessary to return
to the understand or capture stages to
adjust the data or tune the parameters
used in the analytics. A system supporting social media analytics may go
through several iterations before being
truly useful. Data analysts and statisticians help develop and test systems before others use them.
Capture. For a business using social media analytics, the capture stage
helps identify conversations on social
media platforms related to its activities and interests. This is done by collecting enormous amounts of relevant
data across hundreds or thousands
of social media sources using news
feeds and APIs or through crawling.
The capture phase covers popular
platforms (such as Facebook, Foursquare, Google+, LinkedIn, Pinterest, Twitter, Tumblr, and YouTube),
as well as smaller, more specialized
sources (such as Internet forums,
blogs, microblogs, wikis, news sites,
picture-sharing sites, podcasts, and
social-bookmarking sites). An enormous amount of data is archived and
available to meet businesses’ needs.
To prepare a dataset for the understand stage, various preprocessing
steps may be performed, including
data modeling, data and record linking from different sources, stemming,
part-of-speech tagging, feature extraction, and other syntactic and semantic operations that support analysis.
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Being tuned in
to changing
customer tastes
and behavior,
businesses
can anticipate
significant changes
in demand and
adjust accordingly
by ramping
production
up or down.
| J U NE 201 4 | VO L . 5 7 | NO. 6
Information about businesses, users,
and events, as well as user comments
and feedback and other information,
is also extracted for later analytical
modeling and analysis.
The capture stage must balance the
need to find information from all quarters (inclusivity) with a focus on sources that are most relevant and authoritative (exclusivity) to assist in more
refined understanding.
Understand. When a business collects the conversations related to
its products and operations, it must
then assess their meaning and generate metrics useful for decision making—the understand stage. Since the
capture stage gathers data from many
users and sources, a sizeable portion
may be noisy and thus have to be removed prior to 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 statistical methods and other techniques
derived from text and data mining,
natural language processing, machine translation, and network analysis.9 The understand stage provides
information about user sentiment—
how customers feel about a business
and its products—and their behavior,
including the likelihood of, say, purchasing in response to an ad campaign. Many useful metrics and trends
about users can be produced in this
stage, covering their backgrounds,
interests, concerns, and networks of
relationships.
Note the understand stage is the
core of the entire social media analytics process. Its results will have a
significant effect on the information
and metrics in the present stage, thus
the success of future decisions or actions a business might take. Depending on techniques used and information sought, certain analyses may be
preprocessed offline while others are
computed on the fly using data structures optimized for anticipated ad hoc
uses. Analysts and business managers may participate directly in the understand stage when visual analytics
allows them to see various types and
representations of data at once or create visual “slices” that make patterns
more apparent.
contributed articles
Present. In this last stage, the results from different analytics are summarized, evaluated, and shown to users in an easy-to-understand format.
Visualization techniques may be used
to present useful information; one
commonly used interface design is
the visual dashboard, which aggregates and displays information from
multiple sources. Sophisticated visual
analytics go beyond the simple display
of information. By supporting customized views for different users, they help
make sense of large amounts of information, including patterns that are
more apparent to people than to machines. Data analysts and statisticians
may add extra support.
Key Techniques
Social media analytics encompasses
a variety of modeling and analytical
techniques from different fields. Here,
we highlight the most instrumental
in understanding, analyzing, and presenting large amounts of social media
data. Some techniques support several
stages of social media analytics: Sentiment analysis and trend analysis primarily support the understand stage;
topic modeling and social network
analysis have primarily application in
the understand stage but can support
the capture and present stages as well;
and 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.b
It leverages computational linguistics, natural language processing, and
other methods of text analytics to automatically extract user sentiment or
opinions from text sources at any level
of granularity (words or phrases, up
to entire documents). Such subjective
information extracted about people,
products, and services supports predicting the movement of stock markets, identifying market trends, analyzing product defects, and managing
crises. Relatively simple methods
for sentiment analysis include word
b We adopt the view of Pang and Lee15 who
described the terms “opinion mining” and
“sentiment analysis” as having multiple definitions, using them broadly and interchangeably to cover the subjective, textual evaluation
of source materials or their features.
(phrase) counts (the more a product is
mentioned, the more it is assumed to
be liked); “polarity lexicons,” or lists
of positive and negative terms that can
be counted when used, as in, say, text
messages that mention a product by
name;11 and semantic methods that
may compute lexical “distances” between a product’s name and each of
two opposing terms (such as “poor”
and “excellent”) to determine sentiment.25 More complicated approaches distinguish the sentiments about
more than one item referenced in the
same text item (such as a sentence,
paragraph, or 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 involves simple
techniques. Though sentiment analysis is increasingly common, sampling
bias in the data can skew results—even
if large data samples are confused with
unbiased samples—especially in situations where satisfied customers are
silent while those with more extreme
positions loudly voice their opinions.
Topic modeling is used to sift
through large bodies of captured text
to detect dominant themes or topics.
Themes can be used to provide consistent labels to explore the text collection
or build effective navigational interfaces. Themes can also be used to feed
other analytical tasks (such as discovering user interests, detecting emerging
topics in forums or social media postings, and 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 the 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
through the co-occurrence frequencies
of words within a single communication14 or between topics and communities of users.27 Information about the
position of words within messages can
also be considered;26 see Blei4 for a survey of topic modeling.
Social network analysis is used on
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
(edges). Relationships are typically detected through user actions connecting two people directly (such as accepting another user as a “friend”), though
they may be inferred from indirect behaviors creating relationships (such as
voting, tagging, and commenting).
Social network analysis is used to
model social network dynamics and
growth (using such features as network density and locations of new
node attachments) that help monitor
business activity. Social network analysis is the primary technique for identifying key influencers in viral market-
Figure 1. Social media analytics process.
Capture
Understand
Present
Gather data from various sources
Preprocess the data
Extract pertinent information from the data
Remove noisy data (optional)
Perform advanced analytics: opinion mining and
sentiment analysis, topic modeling, social network
analysis, and trend analysis
Summarize and evaluate the findings
from the understand stage
Present the findings
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contributed articles
ing campaigns on Twitter and other
social media platforms. It is also used
to detect subcommunities within a
larger online community (such as
discussion forums), allowing greater
precision in tailoring products and
marketing materials. It is also useful
in predictive modeling, as in marketing campaigns aimed at consumers
assumed most likely to buy a particular product.5
Techniques used by social network
analysis to understand the mathematical structure of graphs18 range from
the simple (such as counting the number of edges a node has or computing
path lengths) to the sophisticated algorithms that compute eigenvectors (as
in Google’s PageRank) to determine
key nodes in a network. This helps determine whom, say, a business might
look to on the basis of expertise and
reputation. The analysis of network
structure significantly predates the
advent of social media, having been
developed mainly for analyzing static
mathematical graphs. Today’s large
and continually changing network
structures demand new technical approaches, especially when real-time
decision support is needed.
Trend analysis is used to predict
future outcomes and behaviors based
on historical data collected over time.
Applications include forecasting the
growth of customer or sales numbers, predicting the effectiveness of
ad campaigns, staying ahead of shifts
in consumer sentiment, and forecast-
ing movement in, say, a stock market.
Trend analysis is based on long-standing statistical methods (such as timeseries analysis and regression analysis1) and other more recent modeling
techniques (such as neural networks12
and support vector machines20).
Visual analytics is “the science of
analytical reasoning facilitated by interactive visual interfaces.”23 Spurred
initially by U.S. defense needs, visualization works across different application areas to support synthesis,
exploration, discovery, and confirmation of insight from data that is 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 very salient dimensions),
the human ability to perceive patterns
and draw conclusions is a key factor
as well. Indeed, when a torrent of information must be addressed quickly, combining machine and human
strengths is critical, in both making
decisions and being able to explain
and justify them. 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 hidden
Figure 2. Radian6 analysis dashboard.
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structure and detail. Computational
methods for data reduction, displaying correl …
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