Delivering Next-Generation Sentiment Intelligence: Tailoring Actionable Analytics to Improve Corporate Decision Making

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1 IHS Industry Sentiment Intelligence July 2013 Delivering Next-Generation Sentiment Intelligence: Tailoring Actionable Analytics to Improve Corporate Decision Making Report Authors Chris Hansen Director, Energy Insight IHS Niels Rosenquist Co-founder and CEO Janys Analytics

2 Contents 3 Executive summary 4 Introduction 4 The future of industry sentiment intelligence 5 Sentiment analysis: Necessary but not sufficient 7 Network analysis and predicting sentiment 7 The power of next-generation sentiment intelligence 8 Case study: Monitoring and Measuring Civil Strife in Bahrain 9 Case study: Predicting the Impact on Business from a Controversial Movie 10 About the authors 10 For more information IHS Industry Sentiment Intelligence ABOUT IHS (WWW.IHS.COM) IHS (NYSE: IHS) is the leading source of information, insight, and analytics in critical areas that shape today s business landscape. Businesses and governments in more than 165 countries around the globe rely on the comprehensive content, expert independent analysis, and flexible delivery methods of IHS to make high-impact decisions and to develop strategies with speed and confidence. IHS has been in business since 1959 and became a publicly traded company on the New York Stock Exchange in Headquartered in Englewood, Colorado, USA, IHS is committed to sustainable, profitable growth and employs more than 6,700 people in 31 countries around the world. IHS is a registered trademark of IHS Inc. All other company and product names may be trademarks of their respective owners. Copyright 2013 IHS Inc. All rights reserved. ELECTRONIC VERSION AVAILABLE This report is available online at and includes a link to the IHS Industry Sentiment Index. 2

3 Executive summary THERE IS A LOT OF ATTENTION THESE DAYS ON how to make sense of the vast amount of sentiment data being generated by social media tools such as Twitter and Facebook. Although great strides are being made to refine the tools and techniques, companies are justifiably hesitant to make decisions based on sentiment data. This article explores cutting-edge developments being made in the science of sentiment analytics. It outlines a model for how companies might begin to integrate sentiment data into the decision-making process and to develop strategies for influencing public sentiment. Integrating sentiment analytics with traditional market intelligence is the foundation on which smart companies are building decision-making models of the future. It is what IHS calls Sentiment Intelligence. Making sense of the massive amounts of unstructured sentiment data requires skills in data management and data science. Big data expertise is essential to being able to deliver answers to complex questions quickly, if not in real time. Extracting the full value of sentiment data requires a deep and nuanced understanding of the particular industry or region of the world. Industry expertise provides the foundation for predicting how sentiment will change in the future. Accurately forecasting changes in sentiment is a valuable input for projecting product sales, market growth, and any number of other metrics that companies rely on to make informed decisions. KEY POINTS OF THIS REPORT INCLUDE: Understanding how complex social networks work is the first step to extracting value from sentiment data. For instance, recent research has revealed that where you are located in the network is more important than how many followers you have. Also, following others is as important as being followed, when it comes to spreading your message and influencing others. And factors such as time of day, location, and even season of the year and climate can influence sentiment data. 3

4 Introduction A British intelligence officer in London is sifting through reports from embassies and monitoring chatter on Twitter and Facebook about political unrest in Bahrain on the anniversary of that country s 2011 uprising. There is so much data that it is difficult to decipher whether the country is heading for more violence and, if so, what the wider implications may be on the region. The officer needs to produce a briefing ASAP with recommendations for protecting UK nationals in the region and to decide whether to pre-position assets for humanitarian intervention. It is critical to be ahead of the curve should the situation escalate. A corporate communications executive for a major energy company in Houston is trying to better understand the public opinion about the industry in general and her firm s upstream capital investment plans in particular. The film Promised Land is about to be released, and there is great concern about whether that film will turn public opinion against hydraulic fracturing initiatives. The executive has to make some major decisions about how the company should respond to the film, and she does not have the time for the traditional focus group approach. But is sentiment analytics ready for prime time? Is it robust enough to provide verifiable results that stand up to the same scrutiny companies apply to other sources of business intelligence? Would a security officer, an energy industry executive, or a Hollywood marketer rely on sentiment data to make crucial, real-time decisions? The answer is probably not yet. But as the science progresses and the tools become more sophisticated, sentiment analytics is destined to play an increasingly important role perhaps a central role in informing decisions. Forward-looking companies that incorporate sentiment analytics today have the opportunity to build a competitive advantage. By monitoring sentiment in real time and responding to market trends early, they can begin to speed up new product development and ultimately capture market share from their slower competitors. By precisely gauging the sentiment of a community, these firms can begin to improve the effectiveness of their sales and marketing messaging, the resilience of their supply chain, or their response to an international crisis. Social media tools should be used with traditional market and product forecasting tools and expert analysis. A marketing executive at a top Hollywood studio is trying to forecast the success of a new release at the box office and to predict follow-on digital video sales. Getting the forecast right is critical because the company is coming off a big box office flop and the executive s boss needs to know how to spend precious marketing dollars on the most promising titles. Unfortunately, prior efforts using social media data to improve the accuracy of revenue predictions failed and the methods used by the consultants were opaque. Although very different, these three decision makers face the same challenge: to find that elusive insight buried within the mountain of social media data that will enable them to make better decisions faster. Delivering on that challenge is the aspiration of the young field of social media or, more broadly, sentiment analytics. It is a very active field, with countless start-ups competing alongside a number of large, well-established companies, all of which are experimenting and pushing the science forward. Of course, incorporating sentiment analytics into the decision-making process is itself a process. Companies have evolved mechanisms and processes for decision making because they work. Social media tools should be used with traditional market and product forecasting tools and expert analysis. It is not an either-or decision; the two are complementary. The future of industry sentiment intelligence The emerging model for understanding market trends, optimizing forecasting, and improving the speed and accuracy of decision making requires a combination of three things: 1. Industry expertise 2. Big data expertise 3. Sentiment and advanced analytics The confluence of these three capabilities is the sweet spot of next-generation industry sentiment intelligence (see Figure 1, Occupying the sweet spot, on page 5). 4

5 When the best of the three approaches overlap, the result is greater than the sum of the individual parts. And that is where the field of industry sentiment intelligence is heading. Industry expertise refers to the deep and nuanced understanding of a particular market. For instance, expertise in the aerospace, automotive, chemicals, electronics, energy, or entertainment markets that spans the extended value chain. IHS has over 2,600 analysts and experts around the world who provide our clients with market insight, forecasts, tools, platforms and deep domain expertise in all these markets and then some. Big data expertise has evolved from managing large quantities of data to giving context to a complex set of information. While advances in the computer sciences and hardware engineering have increased access to larger amounts of data, the real big data revolution is in the new ways data can be stored, manipulated, and analyzed. That is, the ability to deliver answers to complex questions very quickly, if not in real time. IHS is well positioned in big data, given its leadership position in information and analytics services and its recent investments in a multidisciplinary team of world-class computer and data scientists who have expertise in complex networks and massive databases. Sentiment and advanced analytics is the ability to extract meaningful insight from millions of tweets, blogs, Facebook posts, and even Google searches. It requires expertise in managing big data. But, more importantly, sentiment analytics requires expertise to develop and manage a set of sophisticated tools. Over the last two years, IHS has invested in building its capabilities in sentiment analytics, including the development of a number of path-breaking capabilities. It is the power of the products combined with the expertise of the team that gives IHS a leadership position in the field. Companies that can leverage industry and big data expertise in the development of sentiment analytics tools and the understanding of social phenomenon are best positioned to provide the greatest value to their corporate partners. As Figure 2, Sentiment Intelligence market map, on page 6 shows, the highest value is provided by Occupying the sweet spot Relying on sentiment data derived from social media tools like Twitter is interesting but typically not sufficient for making business decisions. But when combined with industry-specific insight and forecasting, together with expertise in managing and manipulating big data, it can provide a strategic edge. The confluence of these three capabilities is greater than the sum of their parts. It is the sweet spot for IHS Industry Intelligence. IHS Sentiment Intelligence Figure 1 Industry expertise Sentiment analytics industry intelligence companies in the top right quadrant. By leveraging sentiment monitoring and social network analyses with deep industry expertise, these companies ask the right questions, analyze the right data, and create the right measures on which their corporate partners can rely to make informed business decisions. Big data expertise Sentiment analysis: Necessary but not sufficient Source: IHS Today, the vast majority of social media analytics tools focus on measuring the sentiment or mood of a community of people. That is, measuring how a group feels about an event, a product, an experience, a person, and so on. Sentiment analytics companies use a variety of tools, including word lists, natural language processing, machine learning, and proprietary algorithms, and they are developing new tools at a rapid pace. The challenge ahead is to refine the techniques for measuring sentiment that predict a group s actions. 5

6 Figure 2 Predicting actions becomes an input for quantitative forecasting. And forecasting is the basis on which companies make informed business decisions. Plenty of work is needed to refine the measurement of sentiment. For instance, solving the problem of how a machine learns to recognize irony or sarcasm or how it handles acronyms, neologisms, and emoticons. Today, when measured against human interpretations of the same sentiment data, the performance of sentiment analytics often comes up short. Case in point: In 2011, IHS evaluated 1.37 billion tweets posted over a two-week period in The research team tested a variety of methods for measuring aggregate sentiment and reached a couple of simple, yet insightful conclusions. First, the commonly used word-list approach searching for words like happy, sad, love, and hate in individual tweets is, for the most part, a poor predictor of aggregate sentiment. It was clear that more advanced techniques were required. Second, the team found that aggregate sentiment results follow distinct temporal, seasonal, geographic, and even climate-based patterns that can be discerned by applying sophisticated analytical approaches. For instance, people living in sunny California tend to be happier than people living in often-overcast New York. And people tend to be happier at the start of their workday, when they are fresh, than at the end of the day, when they are tired. Although applying the most advanced analytics is necessary to accurately capture aggregate sentiment, it is not sufficient to predict the actions a group may take. But predicting those actions is precisely what a corporate executive or an intelligence officer requires to make informed decisions. Take the example of the media executive. Predicting box office revenue and post-theatrical DVD and Blu-ray sales are of critical importance to Hollywood studios. Timely information that can help their supply chain, marketing, and development teams predict those numbers is key. One valuable source is real-time electronic and social media data 6

7 that can be used to gauge audience interest in a film before release and to gauge video sales after moviegoers have seen a film. Working with the company s long-time industry experts and research data, IHS developed two new indices for the film industry: the Audience Anticipation Index (AAI) and the Audience Response Index (ARI). These indices represent composite measures of the buzz about a film before and after its release. The AAI is used to predict opening weekend box office sales, while the ARI is used to assess the long-term success of a title in various other formats over time. These sentiment analytics tools coupled with econometrics techniques provide real-time context for industry decision makers when they are combined with the deep industry understanding IHS provides. Network analysis and predicting sentiment Of course, one of the requirements for predicting actions is to understand how social networks work. That is, how influencers influence and followers follow. Three concepts help predict whether an idea or an opinion will go viral or die on the vine: 1. Centrality how embedded a person is within social networks. 2. Transitivity how tightly someone s followers are connected with each other. 3. Clustering how tightly groups of users are connected. Understanding the strength and direction of these network connections has been shown to predict how a few prominent influencers can impact community sentiment and move the needle on such things as business confidence, demand for a product, and political protest. Companies can harness network analysis as both a defense and an offense. For instance, predicting the strength of the public s reaction to an event or a news item enables a company or a government to plan its response to minimize the fallout. For examples of how to map out a defense, see the case studies highlighting social unrest in Bahrain, on page 8, and a controversial movie about fracking, on page 9. On the flip side, companies can proactively boost the success of their own social media campaigns by selecting individuals who have just the right degree of centrality and transitivity within the targeted communities. This ensures a much higher likelihood that the company s message will spread rather than simply maximizing its number of followers or likes. In one recent example, IHS worked with a large industrial company to identify influencers who had a large number of followers, who followed a large number of people, and whose followers were also well connected. By targeting this select group of influencers, the campaign resulted in the company s message going viral or in social media parlance, achieving escape velocity. Contrary to conventional wisdom, the campaign was not based on the number of followers but on the scientific selection of a small number of very well placed individuals. The power of next-generation sentiment intelligence Social media sentiment analytics is still in its formative years, but it is showing promise as a tool to inform business decisions. Although it would be ill-advised to base decisions solely on social media sentiment data, it is important to start monitoring this data and observing what makes sentiment change over time. To this end, IHS introduced the IHS Sentiment Index in June 2013 to provide a dashboard of sentiment in real-time for the United States. The map is based on a representative sample of Twitter data and can be viewed at the national, state, and metropolitan levels. The IHS Sentiment Index can be customized to meet specific requirements, including measuring sentiment in specific industries and regions. It is equally important to understand how social networks work. Indeed, the combination of advanced sentiment and network analysis is the basis for next-generation social network analytics. These are the tools forward-looking companies are beginning to integrate into their decisionmaking models, along with traditional tools, to enhance market and product forecasting, marketing effectiveness, supply chain management, and other core functional areas. One of the requirements for predicting actions is to understand how social networks work. That is, how influencers influence and followers follow. 7

8 Case Study: Monitoring and Measuring Civil Strife in Bahrain On 17 December 2010, a Tunisian street vendor set himself on fire to protest a tyrannical government, sparking a popular uprising across Northern Africa and the Middle East that came to be known as the Arab Spring. Observers around the world were caught flatfooted by the speed and momentum of the movement. Governments and corporations suddenly awoke to the potential of tracking social media sentiment data to monitor and, perhaps, to predict such spontaneous civil crises in the future. Fast forward to Bahrain, 14 February 2013, exactly two years after the infamous Day of Rage, when protests first erupted in the Persian Gulf island state and long-term, simmering civil unrest followed. To date, more than 80 people have been killed. Between 4 February 2013 and 17 February 2013, IHS monitored Twitter traffic in real time to determine whether social media would provide predictive insights about civil unrest in the days leading up to the anniversary of the Day of Rage and immediately following it. The data included a total of 450,000 tweets, representing 19 percent of all Twitter traffic coming out of Bahrain during that 14-day period. The analysis focused on the topics of military actions, politics, and terrorism by monitoring 129 Arabic terms for bloodshed, ceasefire, demonstration, government, kidnapping, and other key words. Tweets / hour heat map Bahrain terrorism heat map IHS captured Twitter activity as it changed over time and then constructed heat maps as shown in Bahrain terrorism heat map, above to show concentrations of Twitter activity by location within the country. Beyond the normal cyclical changes in Twitter volume associated with the day-night cycle, the data revealed a marked increase in activity starting around 14 February 2013, which coincided with the two-year anniversary of the uprising. This increase is clearly seen on Tweets mentioning terrorism, below. Tweets mentioning terrorism Tweets mentioning terrorism 8 Notably, IHS detected an increase in Twitter traffic relating to the topic of terrorism around the death of Mohammed Asif Khan Afridi, a Bahrain police officer who responded to the unrest. A similar increase in Twitter traffic was recorded in relation to the topics of military and politics around the death of Hussain al-jaziri, a 16-year-old boy killed on 14 February 2013 during the protests. Both events were captured on the heat maps, represented by larger-than-normal hotspots near Riffa, the site of Afridi s funeral, and Daih, the location of al-jaziri s death. The research team also looked at the characteristics of the network of users, specifically the influencers those individuals who shared their thoughts and, by extension, their ideology. In addition, IHS looked at those who followed each other and those who re-tweeted messages. These individuals were aligned with other clusters located further away from them in the network. These interconnected followers were crucial in extending the reach and amplifying the message of the influencers. As such, IHS was able to measure and predict the true influence of the influencers by the size of their re-tweet networks, which is a much more sophisticated measure of influence than basic measures such as the number of followers or re-tweeted messages /03 02/05 02/070 02/09 02/11 02/13 02/15 02/17 02/19 02/03 02/05 Date 02/07 02/09 02/11 02/13 Source: IHS02/15 02/17 02/19 Tweets / hour Source: IHS Date

9 IHS Industry Sentiment Intelligence Case Study: Predicting the Impact on Business from a Controversial Movie Network connections reveal influence On 3 January 2013, Promised Land hit Network connections influence Community detection algorithms revealreveal underlying structure. theaters across the United States. The theme Community detection algorithms reveal underlying structure. of the movie was a small town s reaction to fracking in its backyard. In the weeks Environmental running up to the release,activists several oil and Environmental Activists Politicians Politics Environmental NGOs gas drillers engaged in hydraulic fracturing Environmental NGO grew nervous that public opinion would turn against them because of the movie s Politicians anti-fracking message. They wanted to know Environmental NGOs what the fallout would be and what they needed to do to respond to make sure they could continue to extract natural gas. Politics Environmental NGOs For answers, the drillers turned to IHS, the world s leader in energy-related research Energy Industry Media Politicians and analysis. IHS used sentiment analytics, among other tools, to help the industry plan for damage control. Source: IHS and Janys Analytics Source: IHS Gas Energ IHS analyzed over 20,000 tweets posted That is, how does a tweet reach escape between December 2012 and February 2013 Politics velocity? Achieving escape velocity, it turns that mentioned fracking, Promised Land, and out, is necessary for a message to go viral has (that person s cluster of followers) is not other related terms. The research revealed a and potentially influence sentiment. nearly as important as whether those followers relatively low sentiment score about fracking The research revealed that to reach escape Media velocity the number of followers an influencer re-tweeted the influencer s message outside when measured against the nation s average IHS looked at a small but influential set that person s cluster. That likelihood is based total sentiment. More importantly, it showed of individuals who represented a variety on the lattice-like structure of an influencer s that the public s positive sentiment about of perspectives on fracking including network. So although a message s content fracking moderately decreased following the environmental activists and officials at is important, the linkages between followers movie s release but did not plummet. For the non-governmental organizations (NGOs), are more critical to understanding influence industry, that was good news. politicians, media personalities, and industry and spreading ideas. To use a power industry leaders to see how they were networked analogy, predicting the impact of a power The sentiment results led to questions about and how their follower communities were surge has as much to do with the power grid s the mechanics of how social media networks networked with other communities. The layout as it does the size of the surge itself. influence opinion and shape sentiment. network map Network connections reveal Who are the key influencers of opinion in influence above shows the energy networks The implications for companies looking to IHS constructed from the Twitter data. The influence public sentiment are profound. this area? How do the networks of their followers act proximity of the clusters to each other shows Do not focus on maximizing the number of as catalysts to slingshot the influencers the strength of their relationship and the followers. Instead, focus on building a network messages to social network communities lines show the direct connections. Note that of other influencers and followers who are outside the bounds of their own immediate environmental activists and the oil and gas well connected. In other words, focus on the cluster of followers? clusters are far apart and have few direct links. complexity of your network s lattice. 9

10 About the authors Chris Hansen Director, Energy Insight, IHS Niels Rosenquist Co-founder and CEO, Janys Analytics The lead author of this article, Dr. Hansen specializes in energy sector economics, electricity market reform, and nuclear power, with more than 14 years of experience in the global energy industry. His current role within IHS is to lead acquisition opportunities for the Energy Insight group. In addition, he directs a social media analytics project with a suite of products across several industries. Dr. Hansen was based in Dubai in , where he managed a comprehensive review of the Emirate s energy sector to support Dubai s rapid growth plans. He is a co-creator of the OptGen analysis, IHS CERA s unique tool for applying financial portfolio techniques to power generation. His recent research also includes an analysis of global nuclear power developments, the market potential for small nuclear reactors. Before joining IHS, Dr. Hansen was a Research Fellow at the Oxford Institute for Energy Studies, where he analyzed electricity and gas sector reform and hybrid energy systems in India. Dr. Hansen holds a BSc in Nuclear Engineering from Kansas State University; a Graduate Diploma of Civil Engineering from the University of the Witwatersrand, South Africa; a Master of Science in Technology Policy from MIT; and a Ph.D. in Economic Geography from Oxford University. A contributor to this article, Dr. Rosenquist is a co-founder and CEO of Janys Analytics and a senior advisor to IHS on social media analytics and network science. A trained economist and psychiatrist with appointments at Massachusetts General Hospital and Harvard Medical School, he has published a number of papers in realms as diverse as genetics, social networks, applied economic theory, and social media. Dr. Rosenquist is a graduate of the University of Michigan (B.S.), the University of Pennsylvania (M.D.), and the Wharton School (Ph.D.). For more information As a leading provider of information, insight, and analytics to companies in a variety of industries worldwide, IHS is on the forefront of developing advanced analytical tools for analyzing large, unstructured social media and sentiment data to provide actionable insights for decision makers in near real time. For information on how your company can take advantage of IHS Sentiment Intelligence, us at To access this report and the IHS Sentiment Index go to 10

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