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

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IBMSPSSMBPDM exam Dumps Source : IBM SPSS Modeler - Business(R) partner Data Mining Associate

Test Code : IBMSPSSMBPDM
Test title : IBM SPSS Modeler - Business(R) partner Data Mining Associate
Vendor title : 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 solid has to deliver the remedy rig for its statistics analysts, developers, and business 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 rig for many modern analysts and developers. however for complete their benefits, common open-supply records programs moreover comprise probably time-drinking drawbacks.


Predictive Analytics software market's visionary leaders illuminated by using fresh 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 route 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 moreover captured the properly spot in standards relish geographic footprint and viability of enterprise.

360Quadrants defines predictive analytics as a statistical and records mining solution together with a big number of algorithms and methodologies used for structured as well as unstructured statistics to extract business 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 business influence. These 31 products are classified into Visionary Leaders, Dynamic Differentiators, emerging corporations, and Innovators.

360Quadrants acknowledges IBM Corp, SAS Institute, Inc., SAP SE, dispassionate 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 glide 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 do it effortless to increase, coach, and control fashions. It contains a stout option of tools used for every thing from information prep and blend to lifelike focus. And now Watson Studio is on its mode to your home windows or Mac desktop machine.

Overview of Watson Studio

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

  • SPSS Modeler for records prep, blend, and modeling devoid of coding
  • facts Refinery, a spreadsheet mode tool, that permits quickly, self-provider facts guidance
  • Python, R, and Scala Jupyter Notebooks to create and participate documents that contain are vital 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 vital studies so the conclusion users can ascertain their own insights
  • available Deployment alternate options

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

  • Watson Studio on the IBM Cloud is the simplest route to net complete started. It presents three tiers from a free starter tier with pay-as-you-go compute the entire route to an enterprise edition.
  • Watson Studio local is their in the back of-the-firewall concede 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 fresh 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. complete with a rapidly 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 exist an excellent addition to the Watson Studio household together with their cloud and native flavors.

    The initial leading facets of Watson Studio laptop may exist 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 native data. boost particular person productiveness the employ of a in the neighborhood rescue in client.
  • It gives offline use. exigency to build a mannequin on a airplane devoid of WiFi? Watson Studio computing device will labor 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 native types, switching between deployment alternatives is seamless and not using a enjoyable practising.
  • And just relish the leisure of Watson Studio, anybody — coders or non-coders — will exist capable of rescue 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 moreover consist of projects to systematize your work, information belongings company, and a convenient check for updates feature.

    be allotment of the beta to provide remarks on how you relish these tools, and advocate which other tools they carry to the computing device. The Beta is allotment of the IBM SPSS Modeler Subscription Trial, so when you register for that you just’ll beget 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 usurp hand aspect of the page.

    be a allotment of the discussion on the beta community

    After registering for the beta, exist a allotment of in the discussion on the Watson Studio desktop beta group. here that you can interrogate questions in regards to the beta, supply innovations for future elements, net forum help, and bewitch allotment in useful surveys to beget an outcome on product path. exist allotment of the discussion board privilege 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 exist positive you’ve invested your time and pecuniary resources wisely.

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

    Sources and Perspectives

    To remain on top of trends, I try to talk to current and prospective users as often as I can, and I moreover try to preserve up with researchers and fellow analysts. (You’ll find links for some of the folks I ensue at my BeyeNETWORK channel.) I moreover snare 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 Do you notice as the 3 (or fewer) most famous 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 allotment because “…for me, 2009 is too immediate a time horizon.” This thought applies not only to academic researchers but moreover to anyone who’s thinking strategically, beyond this year’s crop of product releases and business 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 bewitch their views with a grain of salt.

    I’ll relay complete 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 assume that the most difficult challenges of the past are quiet with us, and this is the biggest. People either believe that not much at complete is possible or they believe that more is possible than reality. It is never the case where halt users beget realistic expectations.
  • Portability: Text analytics systems trained on a particular nature of input data Do not typically transfer well to other types of input. Cross-domain issues (e.g., from tidings to medical) are similar to transfer between intra-domain input documents (e.g., from medical tidings 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 field needs is a profound and existent success record 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, convoke center, survey groups. Over the past few years, solutions beget 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 exigency to disclose more a priori business 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 proper 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 business value is even more important.
  • From an isolated to an integrated solution. Toward the halt 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 convoke headquarters applications. This fresh employ cycle – not just for analytics, but for operational integration – provides an quick-witted occasion for text analytics vendors to consider whether to exist a stand-alone application or to be more tightly integrated with partner products and offerings.
  • From a static to a scalable architecture. The stout record in 2008 was scale. Data volumes are going up. Usage requirements are going up. There's no reason to await scalability requirements won’t continue to grow in 2009 and beyond. The winners will exist 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 moreover at business concerns. Aaron is Program Director, ECM Discovery, for IBM Information Management Software and has mighty market and technical insights. I interviewed him final year on “Text Analytics for Legal Compliance.” That exchange is quiet a beneficial read. Here are his thoughts on challenges for 2009:

  • Defining the business case for text analytics. In the current economic situation, organizations are clamping down on fresh projects and more than ever looking for difficult ROI savings to justify investment. To pass the funding bar, text analytics solutions, which typically tumble in the category of fresh projects undertaken for business optimization, exigency to promote with solid business cases that demonstrate hard-dollar operational savings based on proven examples. Given the emerging nature of many text analytics solution areas, this will exist a challenge to growth in 2009.
  • Creating fully interactive exploratory analytics. Most current text analytics approaches faith 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 exist rapidly and seamlessly reconfigured by the business analyst as they explore data sets and interactively refine their understanding of what needs to exist extracted. Further, these applications add pressure to democratize text analytics, as the business user can no longer wait for a linguistic or data processing expert to reconfigure the analytics each time a fresh insight is reached. The fresh 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 business 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 exist widely adopted as a horizontal capability powering broad business 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 exigency to integrate more tightly into information and process management platforms – potentially being subsumed into them as native capability – and solutions will shift to leveraging analytics in conjunction with broader information management and technologies. 2009 will exist a faultfinding year for text analytics to start making this transition.
  • Other replies moreover looked at business 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 exist about two-thirds petite companies and one-third big companies. Now it's mostly larger companies. The upside of this is that stout companies are spending on search and text analytics as they hunt to rescue money and extend efficiency due to lower staffing levels.
  • Technical push: Their propel this year is going to exist 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 sigh 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 nature of entity so that it can deduce other diseases from how they’re described in the text. We'll exist releasing their first version of this by late February/early March and hope to further enhance the appliance 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 disburse on maintaining lists.
  • I await the number of vendors to contract as those with weaker or narrower offerings find it increasingly difficult to sell in this very challenging environment. To do a Go of it in 2009, it seems that at a minimum, vendors exigency to provide: entity extraction, concept extraction and possibly sentiment analysis.
  • Craig Norris, CEO at Attensity, similarly notable three varieties of challenge, in his case relating to technology, implementation and seeing occasion in adverse business conditions:

  • Getting sentiment right: Many of the vendors in the text-analytics space beget pretty beneficial text classification technology – where the application classifies terms based on dictionary entries or predefined lists. The output looks beneficial – but because only terms are looked at, there minister to exist a lot of inaccurate positives – negations are missed, relationships (such as why someone is unhappy) are never found. Many customers beget purchased technology that only goes this far. They will exist challenged this year by consumers of the data who find the data is, in fact, wrong. Natural language processing (NLP) technology relish 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 exist around getting past high-level views of the data (general ratings of sentiment, common views of issues) to the root occasions so that users can bewitch action and companies can net existent value. For example, knowing not only that a customer is unhappy with a product, but moreover that they intend to recrudesce it if they don't net a convoke back or a fix for their problem is faultfinding to being able to remedy a situation and rescue a customer. Being able to know this is where the existent ROI is for text analytics products. This is only possible with an approach that not only finds sentiment but can moreover understand the relationships between the issues and the reasons for the issues.
  • The economy: Certainly the economy will exist 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 rescue them; and revenue growth – understand if customers are having issues ("cries for help") and delight them by acting on those issues. This generates improbable 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 exist 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 bewitch 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 building software and planning road maps accordingly.
  • Customers are consolidating software and needing to settle on one vendor that can wield complete approaches to text – text analytics/mining/business analytics/search/categorization.
  • Collaboration and mobile BI are faultfinding needs, and vendors exigency to exist agile and glide 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, big organizations are forced to preserve up a rapidly pace of business with fewer resources, and it is therefore faultfinding that these organizations beget complete of their content organized correctly so that employees can disburse 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 microscopic in the route of being operationalized within business processes.” Neil continues:

    Yes, there are proofs of concept, precipitate by experienced vendor staff but these [prototypes], in my experience, rarely net adopted by the business 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 business needs is a lofty degree of automation (without the exigency for excessive setup and maintenance) together with clarity in the analysis and the capacity to apply control to the process where needed. This is exactly what Leximancer provides.

    The other major trend I notice is the exigency to do companionable media actionable, and this is something we’ve focused on heavily on their blog. I assume customer attitudes on companionable media or microblogging sites are a leading indicator for the business. The business that waits for these trends to exist reflected in their formal feedback programs may find it is too late to bewitch efficient action.

    Technology and Applications

    I had expected that a higher balance of 2009 “challenges” responses would headquarters on the technology and its applications. Responses I’ve already quoted handle on those areas with perhaps greater attention to business 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, relish 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 heated and will probably net bigger in 2009. There's a lot of confusion though between blogs and other kindly of user-generated content or Web 2.0 data. That's moreover very solution-oriented and not only requires text analytics but moreover data mining, Web scraping capabilities, etc.
  • Sentiment analysis: Not an option anymore in most projects. allotment of Voice of the Customer, blog analysis applications, etc. lofty attribute out-of-the-box results are demanded more and more.
  • Automated translation: moreover 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 moreover 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 companionable media. To fully leverage this data, the key challenges are:

  • Comprehensive and complete data acquisition: Due to the companionable nature of this content, missing documents or authors is relish missing the replies to answers or key voices in the choir.
  • Tuning or recreating touchstone tools to deal with less formal content: Tokenization, sentence segmentation, part-of-speech (POS) tagging, parsing, etc. complete beget different qualities and requirements in the companionable 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 moreover with a yoke of “megatrends” he is beginning to see. Challenges are:

  • Speed to implementation: Clients remain concerned about implementation taking months before realizing any benefit. Immediate payback, more relish a yoke of weeks, seems to exist the mantra!
  • Systems should dynamically and quickly react to changing fresh information: Clearly, clients Do not want to Go through an extensive process of rebuilding their analysis when requirements for analysis change. complete their customers preserve changing their analysis as fresh information unfolds.
  • Finding actionable intelligence in data that is not grammatical: Less time in convoke handling, texting, microscopic 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 resolve such data as well.
  • Ren’s “megatrends” are:

  • The line between structured and unstructured is blurring and a fresh trend is emerging: Users want a fresh approach altogether. As opposed to converting unstructured to structured, users would relish 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 relish that today, no hands went up! They are complete using SQL. So, they notice a fresh trend in analysis going from structured (SQL relish complexity) to unstructured (search relish 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 route to store and reuse search queries and links, and personalize them. This is the beginning of analysis as users beget to now assume (analyze) about content databases, links, companionable 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 bewitch on 2009 text-analytics challenges, Lexalytics’ Jeff Catlin suggested a bake-off to exist 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 quiet 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 net something going for this year – a commercially oriented version of the TREC (Text REtrieval Conference) challenge. remain tuned, and Do consider attending this year’s summit.

    And if you’re a current or prospective text analytics user and would relish to recount me about challenges you kisser or await to face, gladden Do net in handle by email at grimes@altaplana.com or by phone at (301) 270-0795).

  • Seth GrimesSeth Grimes

    Seth is a business intelligence and determination systems expert. He is founding chair of the Text Analytics apex 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. exist positive 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 Go on Twitter, tweet your opinion, and quiet exist heard by those in government? fresh research suggests this is a possibility. 

    The Urban Attitudes Lab at Tufts University has conducted research on accessing "big data" on companionable 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 fresh ways of accessing how people assume about the places they live, labor and play. "We're looking to notice how tapping into companionable 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 motto and feeling, said Hollander. And while formal types of data can exist used as proxies for how gay people are, people openly participate their sentiments on companionable networking sites.

    Twitter and other companionable media sites can moreover provide information in an unobtrusive way. "The credence is that they can capture a potentially more telling and reliable view [of people's] opinions about the world," he said. As an inexact science, companionable science relies on a wide scope 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 moreover interested in extracting data from companionable sites because it can exist done on a 24/7 basis, which means not having to wait for government to administer surveys, relish the Decennial Census. Information from Twitter can moreover exist connected to place; Hollander has approximated that about 10 percent of complete tweets are geotagged to location.

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

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

    For its first study, the Urban Attitudes lab at Tufts University created its own software to isolate and resolve tweets geotagged in the locality of fresh 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 terrible 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 moreover used NVivo qualitative software to resolve 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 stout 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 companionable media sites, to cast a wider net. There is moreover the possibility of analyzing the data for discussions, retweets, and people's positions on issues, although working with big datasets can exist challenging, he said.   

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

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

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

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

    Still, there are limits to this nature of data, Hollander said, because the sample is not representative of the wider population. For instance, younger users are more lively 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 route he gleans public opinion, is moreover not a telling statistical representation of the population, and that there might exist some benefit to the technology. 

    "It would organize what people are motto and it might exist a appliance for planners and other government officials to net 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 fresh 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 allotment of the lab's mission. Hollander is now engaging in discussions with possible government partners, and he is moreover looking at opportunities with Foursquare, a location-based companionable media platform. He said is interested in studying other companionable platforms, including Facebook, Tumblr, Wanelo and Yik Yak. 

    "The credence of really mining the attitudes and opinions of people using the Internet, using companionable media, is really the future," Hollander said. "As government professionals, they really beget to find ways to tap into this information foundation and not just faith on the dilapidated mode ways to understand how things are going." 



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