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IBMSPSSMBPDM IBM SPSS Modeler - Business(R) colleague Data Mining Associate

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Test Code : IBMSPSSMBPDM
Test denomination : IBM SPSS Modeler - Business(R) colleague Data Mining Associate
Vendor denomination : IBM
: 25 existent Questions

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IBM IBM SPSS Modeler -

Open supply and IBM SPSS Modeler | killexams.com existent Questions and Pass4sure dumps

A successful records-pushed firm has to deliver the amend apparatus for its statistics analysts, developers, and trade conclusion users. increasingly, this skill leveraging open-source utility. The R or Python programming languages, together with the Apache Spark and Hadoop facts frameworks, are favourite open-supply apparatus for many modern analysts and developers. however for everyone their benefits, generic open-supply records programs also comprise probably time-drinking drawbacks.


Predictive Analytics software market's visionary leaders illuminated by using current record | killexams.com existent Questions and Pass4sure dumps

IBM tops the list of providers in the predictive analytics software house according to a quadrant currently released by passage of 360Quadrants, adopted intently by means of Oracle and SAP SE. IBM’s SPSS Modeler became rated the most excellent in product fine, reliability, and breadth and depth of offering. IBM SPSS Modeler also captured the properly spot in standards enjoy geographic footprint and viability of enterprise.

360Quadrants defines predictive analytics as a statistical and records mining solution together with a large number of algorithms and methodologies used for structured as well as unstructured statistics to extract trade insights. 

360Quadrants covers 50+ products in the predictive analytics space and places the properly 31 of them in a quadrant reckoning on their fine, reliability, and trade influence. These 31 products are classified into Visionary Leaders, Dynamic Differentiators, emerging corporations, and Innovators.

360Quadrants acknowledges IBM Corp, SAS Institute, Inc., SAP SE, impartial Issac organisation (FICO), Tableau software, Inc., RapidMiner, Inc., Oracle Corp, and Angoss software Corp as Visionary Leaders; TIBCO utility, Inc., Microstrategy, Inc., Alteryx, Inc., assistance Builder, Dataiku, KNIME.COM AG, and NTT facts company, as Innovators; GoodData organization, Microsoft enterprise, Teradata corporation, Sisense, Inc., Predixion application, and Domino information Lab, Inc. as Dynamic Differentiators; and Exago, integrated, AgilOne, QlikTech foreign, and Kognitio Ltd. as rising gamers.

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IBM Watson Studio is Coming to Your computer | killexams.com existent Questions and Pass4sure dumps

instance of a Modeler race operating on Watson Studio computer

IBM Watson Studio is a really potent records science offering. if you’re now not familiar, Watson Studio is an integrated environment designed to obtain it effortless to increase, coach, and control fashions. It contains a broad choice of tools used for every thing from information prep and blend to realistic focus. And now Watson Studio is on its fashion to your home windows or Mac desktop machine.

Overview of Watson Studio

Watson Studio contains a number of apparatus everyone within one built-in environment. the most ordinary apparatus of Watson Studio are:

  • SPSS Modeler for records prep, blend, and modeling devoid of coding
  • facts Refinery, a spreadsheet vogue tool, that permits quickly, self-provider facts guidance
  • Python, R, and Scala Jupyter Notebooks to create and participate documents that accommodate are alive code, equations, visualizations, and explanatory text
  • Neural community Modeler for designing TensorFlow, Keras, PyTorch and Caffe buildings with out coding
  • Interactive Dashboards to carry sharable are alive studies so the conclusion users can determine their own insights
  • available Deployment alternate options

    Watson Studio will soon be available as three deployment alternate options:

  • Watson Studio on the IBM Cloud is the simplest passage to accumulate everyone started. It presents three tiers from a free starter tier with pay-as-you-go compute the entire passage to an enterprise edition.
  • Watson Studio local is their in the back of-the-firewall reply proposing a cozy manner to add Watson Studio capabilities to your commercial enterprise. contains purchase alternatives for a handful of clients or the complete corporation.
  • And coming soon: Watson Studio desktop
  • Introducing Watson Studio computer

    Watson Studio computing device is their current third deployment option within the Watson Studio family. It takes probably the most well-known parts of what's purchasable inside Watson Studio on the cloud and ports it to your windows or Mac desktop for offline use, for evaluation of facts data you don’t wish to add, and for snappy performance. everyone with a lickety-split and straightforward laptop setting up.

    Watson Studio on the computing device will scale from someone consumer the entire means as much as a huge group, and may be an excellent addition to the Watson Studio household together with their cloud and indigenous flavors.

    The initial leading facets of Watson Studio laptop may be a SPSS Modeler canvas along side the information refinery spreadsheet editor. this will comprise backwards compatibility for SPSS Modeler streams.

    benefits of Watson Studio laptop
  • It’s quick, especially for indigenous data. boost particular person productiveness the employ of a in the neighborhood result in client.
  • It gives offline use. necessity to build a mannequin on a airplane devoid of WiFi? Watson Studio computing device will travail devoid of an online connection for weeks at a time.
  • Adhere to security and governance mandates together with your data. Watson Studio desktop allows your entire information to remain for your laptop.
  • limitless modeling devoid of overages. the usage of Watson Studio computer, which you could develop fashions without any incremental can charge. This allows you to construct models iteratively in your desktop, then stream it to Watson Studio on the Cloud to install.
  • It’s one Watson Studio, so no re-researching required. With the identical designs as the cloud and indigenous types, switching between deployment alternatives is seamless and not using a enjoyable practising.
  • And just enjoy the comfort of Watson Studio, anybody — coders or non-coders — will be capable of result together, mix, explore, and model their records in only minutes.
  • Watson Studio computing device’s potent graphing talentsJoin the Beta

    The Beta for Watson Studio is starting now with two tools already ported to the laptop: a refreshed version of SPSS Modeler & data Refinery. it is going to also consist of projects to arrange your work, information belongings company, and a convenient check for updates feature.

    be fraction of the beta to provide remarks on how you enjoy these tools, and advocate which other tools they carry to the computing device. The Beta is fraction of the IBM SPSS Modeler Subscription Trial, so when you register for that you just’ll occupy entry to both downloads.

    After you register, search for the “IBM SPSS Modeler Subscription Trial” card, click down load, then you definitely’ll notice the Watson Studio laptop Beta downloads on the appropriate hand aspect of the page.

    be a fraction of the discussion on the beta community

    After registering for the beta, be a fraction of in the discussion on the Watson Studio desktop beta group. here that you can query questions in regards to the beta, supply innovations for future elements, accumulate forum help, and ensnare fraction in useful surveys to occupy an result on product path. be fraction of the discussion board right here by means of logging in with your IBMid.

    look forward to future bulletins here about this pleasing addition for the Watson Studio household.

    Watson Studio computer challenge organizer

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    Perspectives on Text Analytics in 2009 | killexams.com existent questions and Pass4sure dumps

    You’re reading this article because you’re actively interested in text analytics technology and market trends. Whether you’re a solutions provider or consumer, you want to be sure you’ve invested your time and monetary resources wisely.

    The picture is complex, however, as the text analytics domain is not dominated by any one algorithm or approach, vendor or trade sector. So far, the diversity of options has benefitted potential users; awareness (and realization) of the technology’s capabilities is edifice rapidly in areas such as media monitoring, publishing, customer-experience management and semantic search. Meanwhile, growth remains stalwart in domains that occupy long applied text analytics. These areas comprise life sciences, intelligence and law enforcement, and monetary services. While the text analytics domain isn’t unaffected by current trade conditions, economic pressures could actually spur uptake motivated by a quest for efficiency through automation.

    Sources and Perspectives

    To linger on top of trends, I try to talk to current and prospective users as often as I can, and I also try to maintain up with researchers and fellow analysts. (You’ll find links for some of the folks I ensue at my BeyeNETWORK channel.) I also ensnare up with vendors periodically.

    Because it’s helpful to understand current industry perspectives, I recently invited CEOs, CTOs and thought leaders to respond to the following query:

    What result you notice as the 3 (or fewer) most notable text analytics technology, solution or market challenges in 2009?

    Before relaying answers I received, I’ll mention two meta-replies. First, Marti Hearst, associate professor in the UC Berkeley School of Information, declined to respond, in fraction because “…for me, 2009 is too near a time horizon.” This thought applies not only to academic researchers but also to anyone who’s thinking strategically, beyond this year’s crop of product releases and trade conditions. Next, Lexalytics CEO Jeff Catlin candidly characterized his response as “somewhat self-serving thoughts.” No doubt many of the vendors quoted notice the greatest challenges as residing in areas their tools address, so ensnare their views with a grain of salt.

    I’ll relay everyone views with minimal editing, complete with vendor self-promotion because, after all, commercial products are “where the rubber hits the road” for most enterprise users.

    Text Analytics in 2009

    Let’s start with Maria Milosavljevic, CTO at Capital Markets Cooperative Research Centre, an Australian group that is conducting leading-edge R&D in real-time security-market surveillance. The top 3 for Maria are:

  • Expectations: Unfortunately I mediate that the most difficult challenges of the past are still with us, and this is the biggest. People either believe that not much at everyone is feasible or they believe that more is feasible than reality. It is never the case where linger users occupy realistic expectations.
  • Portability: Text analytics systems trained on a particular character of input data result not typically transfer well to other types of input. Cross-domain issues (e.g., from news to medical) are similar to transfer between intra-domain input documents (e.g., from medical news to medical journals).
  • Data quality: Data is never clean. Garbage in = garbage out!
  • Breck Baldwin of Alias-i, author of LingPipe natural language processing software, is very to-the-point in his reply, with a sort of twist on Maria’s expectations response.

    #1 thing the sphere needs is a profound and existent success chronicle of text analytics.

    The reply from Sid Banerjee, CEO of Clarabridge, looks at factors related to scaling text analytics to the enterprise, “the expansion of text analytics across a few dimensions.” His reply:

  • From a functional to an enterprise imperative. In 2006-2008, text analytics was deployed to marketing, convene center, survey groups. Over the past few years, solutions occupy morphed from those that serve one group to an offering that supports multiple groups, and now customers are looking, on day one, to pick solutions that can demonstrate cross-functional value. The selling cycles are more complex, the solutions necessity to demonstrate more a priori trade value and relevance, and at the identical time, because the solutions are not just deployed in one area, IT wants to know more how the solution is going to providential with "enterprise" standards. In total: selling is now more complex, more constituencies are involved in the decision, but at the identical time articulating and demonstrating trade value is even more important.
  • From an isolated to an integrated solution. Toward the linger of 2008, they started seeing more interest from companies and partners looking to seamlessly integrate the results of text analytics back into operational systems, i.e., process customer verbatims and merge the categorized, scored results back into convene heart applications. This current employ cycle – not just for analytics, but for operational integration – provides an absorbing opportunity for text analytics vendors to reckon whether to be a stand-alone application or to be more tightly integrated with colleague products and offerings.
  • From a static to a scalable architecture. The broad chronicle in 2008 was scale. Data volumes are going up. Usage requirements are going up. There's no understanding to anticipate scalability requirements won’t continue to grow in 2009 and beyond. The winners will be those who can notice beyond today's data and user volumes and design for an order or two more magnitude in their offerings, predicting this inescapable future.
  • Aaron B. Brown of IBM similarly looks at architectural issues and also at trade concerns. Aaron is Program Director, ECM Discovery, for IBM Information Management Software and has much market and technical insights. I interviewed him terminal year on “Text Analytics for Legal Compliance.” That exchange is still a salubrious read. Here are his thoughts on challenges for 2009:

  • Defining the trade case for text analytics. In the current economic situation, organizations are clamping down on current projects and more than ever looking for difficult ROI savings to warrant investment. To pass the funding bar, text analytics solutions, which typically drop in the category of current projects undertaken for trade optimization, necessity to Come with solid trade cases that demonstrate hard-dollar operational savings based on proven examples. Given the emerging nature of many text analytics solution areas, this will be a challenge to growth in 2009.
  • Creating fully interactive exploratory analytics. Most current text analytics approaches rely on extensive design-time configuration and customization. This has limited its applicability to employ cases where categories of extracted entities, etc. are understood up front. An emerging class of investigation-centric applications, such as eDiscovery, requires text analytics that can be rapidly and seamlessly reconfigured by the trade analyst as they explore data sets and interactively refine their understanding of what needs to be extracted. Further, these applications add pressure to democratize text analytics, as the trade user can no longer wait for a linguistic or data processing expert to reconfigure the analytics each time a current insight is reached. The current challenge for text analytics is to enable extraction and analysis that are near instantaneous and at the identical time are reconfigurable at interactive speeds by a trade user without specialized text or linguistic expertise.
  • Expanding mainstream employ cases for text analytics. To date, text analytics has had stalwart traction in inescapable niche solution markets. However, it has yet to be widely adopted as a horizontal capability powering broad trade optimization employ cases in content-centric (e.g., ECM), process-centric (e.g., BPM) or data-centric (e.g., BI) applications. As the market moves toward mainstream usage, text analytics technologies (and their relatives in text classification and search) will necessity to integrate more tightly into information and process management platforms – potentially being subsumed into them as indigenous capability – and solutions will shift to leveraging analytics in conjunction with broader information management and technologies. 2009 will be a critical year for text analytics to start making this transition.
  • Other replies also looked at trade conditions and at experiences in the adoption and employ of text technologies. Lexalytics CEO Jeff Catlin foresees:

  • The penniless economy will shift the sale of text analytics to larger companies. We're already seeing this in their sales, which used to be about two-thirds petite companies and one-third large companies. Now it's mostly larger companies. The upside of this is that broad companies are spending on search and text analytics as they search to deliver money and augment efficiency due to lower staffing levels.
  • Technical push: Their shove this year is going to be on empowering the customers with easy-to-use linguistic tools that will allow users to build and deploy some of their own text analytics bits and pieces using their core frameworks. The first of these for us is a user-driven entity recognizer that will allow users to notice up domain-specific text (let’s instruct medical text) with entities (diseases); and then after marking up a hundred or so stories, the system will build a "model" or recognizer for that character of entity so that it can deduce other diseases from how they’re described in the text. We'll be releasing their first version of this by late February/early March and hope to further enhance the tool over the course of the year to further empower the users. Their initial market research indicates that publishers/media would find such tools very valuable because of the amount of time/money they spend on maintaining lists.
  • I anticipate the number of vendors to constrict as those with weaker or narrower offerings find it increasingly difficult to sell in this very challenging environment. To obtain a retrograde of it in 2009, it seems that at a minimum, vendors necessity to provide: entity extraction, concept extraction and possibly sentiment analysis.
  • Craig Norris, CEO at Attensity, similarly noted three varieties of challenge, in his case relating to technology, implementation and seeing opportunity in adverse trade conditions:

  • Getting sentiment right: Many of the vendors in the text-analytics space occupy pretty salubrious text classification technology – where the application classifies terms based on dictionary entries or predefined lists. The output looks salubrious – but because only terms are looked at, there tend to be a lot of unfounded positives – negations are missed, relationships (such as why someone is unhappy) are never found. Many customers occupy purchased technology that only goes this far. They will be challenged this year by consumers of the data who find the data is, in fact, wrong. Natural language processing (NLP) technology enjoy Attensity's not only finds sentiment; it can find sentiment in context, determining accurately whether it's negative or positive, the degree [or intensity] of it, and the “why” behind it.
  • Getting to Action: Another challenge this year will be around getting past high-level views of the data (general ratings of sentiment, generic views of issues) to the root occasions so that users can ensnare action and companies can accumulate existent value. For example, knowing not only that a customer is unhappy with a product, but also that they intend to recrudesce it if they don't accumulate a convene back or a fix for their problem is critical to being able to remedy a situation and deliver a customer. Being able to know this is where the existent ROI is for text analytics products. This is only feasible with an approach that not only finds sentiment but can also understand the relationships between the issues and the reasons for the issues.
  • The economy: Certainly the economy will be a challenge for any technology solution vendor this year. Their offering has proven to enable two things to assist companies in a tight economy: revenue preservation – find out if customers are going leave, understand why and deliver them; and revenue growth – understand if customers are having issues ("cries for help") and delight them by acting on those issues. This generates wonderful word of mouth and ultimately growth.
  • Solutions Focus

    Vendor prognosticators with a solutions focus comprise Keith Collins, CTO at SAS, and Manya Mayes, SAS Chief Text Mining Strategist, notice as challenges for 2009:

  • A broader set of vertical/horizontal offerings including more automated unstructured (text, voice, image) capabilities must be delivered for customer/product/competitive intelligence. SAS is doing this, for example, with SAS Text Miner integration into SAS Warranty Analysis. Automated capabilities comprise graphics, sentiment analysis, net promoter scores, and key performance indicators (KPIs) from text analysis results.
  • Solutions providers must ensnare text analytics and search across the breadth of their offerings: For SAS, text+DI [data integration], text+BI[business intelligence], text+DataFlux, text+JMP, text+SOO [Service Operations Optimization], etc. SAS customers are requesting these capabilities, and they are edifice software and planning road maps accordingly.
  • Customers are consolidating software and needing to settle on one vendor that can ply everyone approaches to text – text analytics/mining/business analytics/search/categorization.
  • Collaboration and mobile BI are critical needs, and vendors necessity to be agile and race technology swiftly to meet the needs of consumers.
  • Yves Schabes, President of Teragram, a SAS company, focuses his response on one particular challenge – increasing workers’ efficiency:

    Given the current economic situation, large organizations are forced to maintain up a lickety-split pace of trade with fewer resources, and it is therefore critical that these organizations occupy everyone of their content organized correctly so that employees can spend more time fulfilling their jobs and less time searching for information on their enterprise's system.

    Neil Hartley, CEO of Leximancer, sees “an industry that has been around for a long time and yet has seen exiguous in the passage of being operationalized within trade processes.” Neil continues:

    Yes, there are proofs of concept, Run by experienced vendor staff but these [prototypes], in my experience, rarely accumulate adopted by the trade a) because of the setup and maintenance required, and b) because the source data vocabulary changes making the initial setup redundant. Autonomy is the exception, but their deployments are largely search/retrieval-based and less qualitative.

    What the trade needs is a elevated degree of automation (without the necessity for extreme setup and maintenance) together with clarity in the analysis and the aptitude to apply control to the process where needed. This is exactly what Leximancer provides.

    The other major trend I notice is the necessity to obtain social media actionable, and this is something we’ve focused on heavily on their blog. I mediate customer attitudes on social media or microblogging sites are a leading indicator for the business. The trade that waits for these trends to be reflected in their formal feedback programs may find it is too late to ensnare efficacious action.

    Technology and Applications

    I had expected that a higher balance of 2009 “challenges” responses would heart on the technology and its applications. Responses I’ve already quoted paw on those areas with perhaps greater attention to trade and market concerns. Eric Martin, Product Marketing Manager at SPSS, focused solely on solutions, which to me reflects both Eric’s assurance in his company’s market position and his own background: Eric earned a Ph.D. in immunology and cellular biology and, enjoy many others, started using text mining on biomedical articles before joining text analytics pioneer LexiQuest, which was later acquired by SPSS. Challenge areas Eric sees for 2009 are:

  • Blog analysis: Already smarting and will probably accumulate bigger in 2009. There's a lot of confusion though between blogs and other benevolent of user-generated content or Web 2.0 data. That's also very solution-oriented and not only requires text analytics but also data mining, Web scraping capabilities, etc.
  • Sentiment analysis: Not an option anymore in most projects. fraction of Voice of the Customer, blog analysis applications, etc. elevated character out-of-the-box results are demanded more and more.
  • Automated translation: also getting more and more market traction prior to text analysis.
  • Matthew Hurst of Microsoft Live Labs is likewise a techie at heart, and he’s also very interested in user-generated and other online content. Matthew says, “As reported elsewhere in research and industry literature, the majority of textual data being published online today is from the many genres of social media. To fully leverage this data, the key challenges are:

  • Comprehensive and complete data acquisition: Due to the social nature of this content, missing documents or authors is enjoy missing the replies to answers or key voices in the choir.
  • Tuning or recreating gauge tools to deal with less formal content: Tokenization, sentence segmentation, part-of-speech (POS) tagging, parsing, etc. everyone occupy different qualities and requirements in the social space.
  • Development and upkeep of broad (product) ontologies: This is a key requirement for grounding any analytics.
  • Lastly, Ren Mohan, Co-Chairman and CTO of IxReveal, replied not only with the three challenges his company hears most often from their clients, but also with a brace of “megatrends” he is nascence to see. Challenges are:

  • Speed to implementation: Clients remain concerned about implementation taking months before realizing any benefit. Immediate payback, more enjoy a brace of weeks, seems to be the mantra!
  • Systems should dynamically and quickly react to changing current information: Clearly, clients result not want to retrograde through an extensive process of rebuilding their analysis when requirements for analysis change. everyone their customers maintain changing their analysis as current information unfolds.
  • Finding actionable intelligence in data that is not grammatical: Less time in convene handling, texting, exiguous patience, quick notes by data sources – this implies an increasing amount of text data has embedded meanings but is less grammatical. So, their clients are asking us to assist them analyze such data as well.
  • Ren’s “megatrends” are:

  • The line between structured and unstructured is blurring and a current trend is emerging: Users want a current approach altogether. As opposed to converting unstructured to structured, users would enjoy to notice it the other way, and they want it quickly and easily. In the ISO [insurance industry] fraud/claims management conference, the keynote presenter asked how many wished their systems could respond with Google-like access and ease; 100% of the audience raised their hands. When he asked how many of their systems behave enjoy that today, no hands went up! They are everyone using SQL. So, they notice a current trend in analysis going from structured (SQL enjoy complexity) to unstructured (search enjoy simplicity)!
  • Thus far, text analytics has been available only for mega corporations! Now, the trend is catching on in the consumer/desktop user world as well. Products like uReka! from IxReveal and SearchWiki from Google are examples. They give users a passage to store and reuse search queries and links, and personalize them. This is the nascence of analysis as users occupy to now mediate (analyze) about content databases, links, social networks, etc.
  • Ren’s conclusion: “These are truly exciting times!” Indeed.

    A Live Challenge?

    Here are thoughts on one more potential challenge for 2009, a live challenge, of a different variety.

    As an aside while forwarding his ensnare on 2009 text-analytics challenges, Lexalytics’ Jeff Catlin suggested a bake-off to be held at the 2009 Text Analytics Summit, which is slated for June 1-2 (preceded by tutorials) in Boston. Jeff says in his blog, “Our market is still dominated by too much flashy marketing and not enough down in the dirt numbers for ‘apples to apples’ comparisons.” Jeff is right, although I’ll add that I believe that most of the flashy marketing actually fronts very capable products. Perhaps they can accumulate something going for this year – a commercially oriented version of the TREC (Text REtrieval Conference) challenge. linger tuned, and result reckon attending this year’s summit.

    And if you’re a current or prospective text analytics user and would enjoy to narrate me about challenges you face or anticipate to face, gladden result accumulate in paw by email at grimes@altaplana.com or by phone at (301) 270-0795).

  • Seth GrimesSeth Grimes

    Seth is a trade intelligence and determination systems expert. He is founding chair of the Text Analytics climax and principal consultant at Washington, D.C., based Alta Plana Corporation. Seth consults, writes, and speaks on information-systems strategy, data management and analysis systems, IT industry trends, and emerging analytical technologies. Seth chairs the Sentiment Analysis Symposium and the Text Analytics Summit.

    Editor’s Note: More articles and resources are available in Seth's BeyeNETWORK Expert Channel. be sure to visit today!

  • Recent articles by Seth Grimes


    Can Government Mine Tweets to Assess Public Opinion? | killexams.com existent questions and Pass4sure dumps

    What if instead of going to a city meeting, you could retrograde on Twitter, tweet your opinion, and still be heard by those in government? current research suggests this is a possibility. 

    The Urban Attitudes Lab at Tufts University has conducted research on accessing "big data" on social networking sites for civic purposes, according to Justin Hollander, associate professor in the Department of Urban and Environmental Policy and Planning at Tufts.  

    About six months ago, Hollander began researching current ways of accessing how people mediate about the places they live, travail and play. "We're looking to notice how tapping into social media data to understand attitudes and opinions can benefit both urban planning and public policy," he said.   

    Harnessing natural comments -- there are about one billion tweets per day -- could assist governments learn what people are maxim and feeling, said Hollander. And while formal types of data can be used as proxies for how pleased people are, people openly participate their sentiments on social networking sites.

    Twitter and other social media sites can also provide information in an unobtrusive way. "The scheme is that they can capture a potentially more valid and dependable view [of people's] opinions about the world," he said. As an inexact science, social science relies on a wide attain of data sources to inform research, including surveys, interviews and focus groups; but people respond to being the topic of study, possibly affecting outcomes, Hollander said. 

    Hollander is also interested in extracting data from social sites because it can be done on a 24/7 basis, which means not having to wait for government to administer surveys, enjoy the Decennial Census. Information from Twitter can also be connected to place; Hollander has approximated that about 10 percent of everyone tweets are geotagged to location.

    In its first study earlier this year, the lab looked at using broad data to learn about people's sentiments and civic interests in current Bedford, Mass., comparing Twitter messages with the city's published meeting minutes. 

    To extract tweets over a six-week term from February to April, researchers used the lab's own software to capture 122,186 tweets geotagged within the city that also had words pertaining to the current Bedford area. Hollander said anyone can accumulate API information from Twitter to also mine data from an area as petite as a neighborhood containing a brace hundred houses. 

    For its first study, the Urban Attitudes lab at Tufts University created its own software to sequester and analyze tweets geotagged in the area of current Bedford, Mass.

    Researchers used IBM's SPSS Modeler software, comparing this to custom-designed software, to leverage a sentiment dictionary of nearly 3,000 words, assigning a sentiment score to each phrase -- ranging from -5 for injurious feelings to +5 for feelings of elation. The lab did this for the Twitter messages, and found that about 7 percent were positive versus 5.5 percent negative, and correspondingly in the minutes, 1.7 percent were positive and .7 percent negative. In total, about 11,000 messages contained sentiments. 

    The lab also used NVivo qualitative software to analyze 24 key words in a one-year sample of the city's meeting minutes. By searching for the identical words in Twitter posts, the researchers found that "school," "health," "safety," "parks," "field" and "children" were used frequently across both mediums.

    Comparing tweets with the minutes, the lab's prefatory findings presented at a Chicago conference on broad data intimate that slightly more sentiment was expressed in the Twitter realm, and that people in both the formal and informal contexts had expressed similar interests. 

    Hollander said other researchers or applications could expand the timeline for extracting data from social media sites, to cast a wider net. There is also the possibility of analyzing the data for discussions, retweets, and people's positions on issues, although working with large datasets can be challenging, he said.   

    Maria Pina-Rocha, director of Management Information Systems for current Bedford, said there's a municipal employ for the study's sentiment-combing software, especially for current Bedford's tourism business. Yet Pina-Rocha said she has reservations about whether Twitter is the right forum to assess opinions on city-related issues, and to what extent opinions shared there are done so for other reasons, such as a passage to blow off steam. 

    "I would witness at the data and it would play a role, but I'm not sure what weight I would give it," Pina-Rocha said. "I mediate it is worthwhile to consider." 

    Still, Pina-Rocha said, social networking sites are the future public forums. "That's the passage we're going these days -- people occupy their opinions and they are putting it out there." 

    Using the study's passage of categorizing sentiments on Twitter, Hollander said governments can mine the data to reply specific questions, such as: What is the overall feeling of a proposed municipal bike path? In this way, supplementing traditional information-gathering methods, he said, a city could accumulate a sense of people's feelings about a project. 

    Still, there are limits to this character of data, Hollander said, because the sample is not representative of the wider population. For instance, younger users are more dynamic on these sites, according to Philip Chester, who serves as town planner for Lebanon, Conn., who sticks mainly to e-mail. Even so, he acknowledged that a public hearing, which is one passage he gleans public opinion, is also not a valid statistical representation of the population, and that there might be some benefit to the technology. 

    "It would organize what people are maxim and it might be a tool for planners and other government officials to accumulate some of the pulse of what's out there," said Chester, who studied alongside Hollander in graduate school.  

    Next up for the lab is a current study contrasting Twitter posts from four Massachusetts cities with the recent election results. 

    As for using the technology to assist government, Hollander said that is fraction of the lab's mission. Hollander is now engaging in discussions with feasible government partners, and he is also looking at opportunities with Foursquare, a location-based social media platform. He said is interested in studying other social platforms, including Facebook, Tumblr, Wanelo and Yik Yak. 

    "The scheme of really mining the attitudes and opinions of people using the Internet, using social media, is really the future," Hollander said. "As government professionals, they really occupy to find ways to tap into this lore basis and not just rely on the former vogue ways to understand how things are going." 



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