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

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

Test Code : IBMSPSSMBPDM
Test cognomen : IBM SPSS Modeler - Business(R) confederate Data Mining Associate
Vendor cognomen : IBM
: 25 real Questions

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IBM IBM SPSS Modeler -

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

A successful records-pushed solid has to deliver the correct tackle 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 tackle for many modern analysts and developers. however for outright their benefits, common open-supply records programs moreover comprise probably time-drinking drawbacks.


Predictive Analytics software market's visionary leaders illuminated by using novel record | killexams.com real 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 moreover captured the properly spot in standards like 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 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 real Questions and Pass4sure dumps

instance of a Modeler go 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 fabricate it effortless to increase, coach, and control fashions. It contains a broad selection of tools used for every thing from information prep and blend to graphic focus. And now Watson Studio is on its manner to your home windows or Mac desktop machine.

Overview of Watson Studio

Watson Studio contains a number of tackle outright within one built-in environment. the most ordinary tackle 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 partake documents that hold are animate 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 animate studies so the conclusion users can determine their own insights
  • available Deployment alternate options

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

  • Watson Studio on the IBM Cloud is the simplest passage to acquire outright 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 novel 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. outright with a snappy 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 breathe an excellent addition to the Watson Studio household together with their cloud and endemic flavors.

    The initial leading facets of Watson Studio laptop may breathe 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 endemic data. boost particular person productiveness the employ of a in the neighborhood do 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 endemic types, switching between deployment alternatives is seamless and not using a enjoyable practising.
  • And just like the rest of Watson Studio, anybody — coders or non-coders — will breathe capable of do 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 order your work, information belongings company, and a convenient check for updates feature.

    be piece of the beta to provide remarks on how you like these tools, and advocate which other tools they carry to the computing device. The Beta is piece 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 view the Watson Studio laptop Beta downloads on the commandeer hand aspect of the page.

    be a piece of the discussion on the beta community

    After registering for the beta, breathe a piece of in the discussion on the Watson Studio desktop beta group. here that you can examine questions in regards to the beta, supply innovations for future elements, acquire forum help, and retract piece in useful surveys to beget an upshot on product path. breathe piece of the discussion board birthright 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 real 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 breathe sure you’ve invested your time and financial 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 structure rapidly in areas such as media monitoring, publishing, customer-experience management and semantic search. Meanwhile, growth remains stout in domains that beget long applied text analytics. These areas comprise life sciences, intelligence and law enforcement, and financial 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 stay on top of trends, I try to talk to current and prospective users as often as I can, and I moreover try to retain up with researchers and fellow analysts. (You’ll find links for some of the folks I succeed at my BeyeNETWORK channel.) I moreover 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 carry out you view as the 3 (or fewer) most requisite 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 piece because “…for me, 2009 is too nigh 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 trade conditions. Next, Lexalytics CEO Jeff Catlin candidly characterized his response as “somewhat self-serving thoughts.” No doubt many of the vendors quoted view the greatest challenges as residing in areas their tools address, so retract their views with a grain of salt.

    I’ll relay outright 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 deem that the most difficult challenges of the past are still with us, and this is the biggest. People either believe that not much at outright is viable or they believe that more is viable than reality. It is never the case where terminate users beget realistic expectations.
  • Portability: Text analytics systems trained on a particular nature of input data carry out not typically transfer well to other types of input. Cross-domain issues (e.g., from intelligence to medical) are similar to transfer between intra-domain input documents (e.g., from medical intelligence 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 real success narrative 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, muster 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 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 appropriate 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 terminate 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 muster headquarters applications. This novel employ cycle – not just for analytics, but for operational integration – provides an titillating chance for text analytics vendors to deem whether to breathe a stand-alone application or to be more tightly integrated with confederate products and offerings.
  • From a static to a scalable architecture. The broad narrative in 2008 was scale. Data volumes are going up. Usage requirements are going up. There's no understanding to hope scalability requirements won’t continue to grow in 2009 and beyond. The winners will breathe those who can view beyond today's data and user volumes and design for an order or two more magnitude in their offerings, predicting this certain future.
  • Aaron B. Brown of IBM similarly looks at architectural issues and moreover at trade concerns. Aaron is Program Director, ECM Discovery, for IBM Information Management Software and has Great 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 novel projects and more than ever looking for arduous ROI savings to justify investment. To pass the funding bar, text analytics solutions, which typically drop in the category of novel projects undertaken for trade optimization, necessity to further 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 breathe a challenge to growth in 2009.
  • Creating fully interactive exploratory analytics. Most current text analytics approaches reliance 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 breathe rapidly and seamlessly reconfigured by the trade analyst as they explore data sets and interactively refine their understanding of what needs to breathe 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 novel insight is reached. The novel 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 stout traction in certain niche solution markets. However, it has yet to breathe 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 endemic capability – and solutions will shift to leveraging analytics in conjunction with broader information management and technologies. 2009 will breathe a faultfinding year for text analytics to start making this transition.
  • Other replies moreover looked at trade conditions and at experiences in the adoption and employ of text technologies. Lexalytics CEO Jeff Catlin foresees:

  • The poverty-stricken economy will shift the sale of text analytics to larger companies. We're already seeing this in their sales, which used to breathe about two-thirds petite companies and one-third big 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 rescue money and multiply efficiency due to lower staffing levels.
  • Technical push: Their push this year is going to breathe 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 label up domain-specific text (let’s shriek 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 breathe releasing their first version of this by late February/early March and hope to further enhance the instrument 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 hope 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 fabricate a go 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 chance in adverse trade conditions:

  • Getting sentiment right: Many of the vendors in the text-analytics space beget 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 watch to breathe a lot of False 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 breathe challenged this year by consumers of the data who find the data is, in fact, wrong. Natural language processing (NLP) technology like 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 breathe around getting past high-level views of the data (general ratings of sentiment, common views of issues) to the root antecedent so that users can retract action and companies can acquire real 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 acquire a muster 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 real ROI is for text analytics products. This is only viable 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 breathe a challenge for any technology solution vendor this year. Their offering has proven to enable two things to capitalize 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 extraordinary 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, view as challenges for 2009:

  • A broader set of vertical/horizontal offerings including more automated unstructured (text, voice, image) capabilities must breathe 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 retract 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 structure software and planning road maps accordingly.
  • Customers are consolidating software and needing to settle on one vendor that can exploit outright approaches to text – text analytics/mining/business analytics/search/categorization.
  • Collaboration and mobile BI are faultfinding needs, and vendors necessity to breathe agile and go 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 retain up a snappy pace of trade with fewer resources, and it is therefore faultfinding that these organizations beget outright 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 Little 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 acquire 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 lofty degree of automation (without the necessity for inordinate setup and maintenance) together with clarity in the analysis and the faculty to apply control to the process where needed. This is exactly what Leximancer provides.

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

    Technology and Applications

    I had expected that a higher symmetry of 2009 “challenges” responses would headquarters 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 aplomb in his company’s market position and his own background: Eric earned a Ph.D. in immunology and cellular biology and, like 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 red and will probably acquire bigger in 2009. There's a lot of confusion though between blogs and other kind 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. piece of Voice of the Customer, blog analysis applications, etc. lofty quality 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 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 like missing the replies to answers or key voices in the choir.
  • Tuning or recreating criterion tools to deal with less formal content: Tokenization, sentence segmentation, part-of-speech (POS) tagging, parsing, etc. outright beget 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 moreover with a couple of “megatrends” he is tower to see. Challenges are:

  • Speed to implementation: Clients remain concerned about implementation taking months before realizing any benefit. Immediate payback, more like a couple of weeks, seems to breathe the mantra!
  • Systems should dynamically and quickly react to changing novel information: Clearly, clients carry out not want to go through an extensive process of rebuilding their analysis when requirements for analysis change. outright their customers retain changing their analysis as novel information unfolds.
  • Finding actionable intelligence in data that is not grammatical: Less time in muster handling, texting, Little 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 capitalize them resolve such data as well.
  • Ren’s “megatrends” are:

  • The line between structured and unstructured is blurring and a novel trend is emerging: Users want a novel approach altogether. As opposed to converting unstructured to structured, users would like to view 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 like that today, no hands went up! They are outright using SQL. So, they view a novel trend in analysis going from structured (SQL like complexity) to unstructured (search like 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 tower of analysis as users beget to now deem (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 retract on 2009 text-analytics challenges, Lexalytics’ Jeff Catlin suggested a bake-off to breathe 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 acquire something going for this year – a commercially oriented version of the TREC (Text REtrieval Conference) challenge. stay tuned, and carry out deem attending this year’s summit.

    And if you’re a current or prospective text analytics user and would like to uncover me about challenges you visage or hope to face, tickle carry out acquire in paw by email at grimes@altaplana.com or by phone at (301) 270-0795).

  • Seth GrimesSeth Grimes

    Seth is a trade intelligence and decision systems expert. He is founding chair of the Text Analytics zenith 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. breathe sure to visit today!

  • Recent articles by Seth Grimes


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

    What if instead of going to a city meeting, you could go on Twitter, tweet your opinion, and still breathe heard by those in government? novel 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 novel ways of accessing how people deem about the places they live, travail and play. "We're looking to view how tapping into social media data to understand attitudes and opinions can capitalize both urban planning and public policy," he said.   

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

    Twitter and other social media sites can moreover provide information in an unobtrusive way. "The thought is that they can capture a potentially more sound and amenable view [of people's] opinions about the world," he said. As an inexact science, social science relies on a wide compass 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 social sites because it can breathe done on a 24/7 basis, which means not having to wait for government to administer surveys, like the Decennial Census. Information from Twitter can moreover breathe connected to place; Hollander has approximated that about 10 percent of outright 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 novel 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 moreover had words pertaining to the novel Bedford area. Hollander said anyone can acquire API information from Twitter to moreover mine data from an zone as petite as a neighborhood containing a couple hundred houses. 

    For its first study, the Urban Attitudes lab at Tufts University created its own software to seclude and resolve tweets geotagged in the zone of novel 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 poverty-stricken feelings to +5 for feelings of elation. The lab did this for the Twitter messages, and establish 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 establish that "school," "health," "safety," "parks," "field" and "children" were used frequently across both mediums.

    Comparing tweets with the minutes, the lab's preliminary findings presented at a Chicago conference on broad data hint 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 moreover the possibility of analyzing the data for discussions, retweets, and people's positions on issues, although working with big datasets can breathe challenging, he said.   

    Maria Pina-Rocha, director of Management Information Systems for novel Bedford, said there's a municipal employ for the study's sentiment-combing software, especially for novel Bedford's tourism business. Yet Pina-Rocha said she has reservations about whether Twitter is the birthright 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 explore at the data and it would play a role, but I'm not sure what weight I would give it," Pina-Rocha said. "I deem 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 beget 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 ardor of a proposed municipal bike path? In this way, supplementing traditional information-gathering methods, he said, a city could acquire 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 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 moreover not a sound statistical representation of the population, and that there might breathe some capitalize to the technology. 

    "It would organize what people are motto and it might breathe a instrument for planners and other government officials to acquire 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 novel study contrasting Twitter posts from four Massachusetts cities with the recent election results. 

    As for using the technology to capitalize government, Hollander said that is piece of the lab's mission. Hollander is now engaging in discussions with viable government partners, and he is moreover 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 thought 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 beget to find ways to tap into this information base and not just reliance on the veteran mode ways to understand how things are going." 



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    The ambitious PVP based survival game Rend will soon be entering the next phase of its release cycle by exiting Alpha and entering Early Access on Steam on July...

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    Large scale battles have been around in some PVP games for a while on PC and some console games, but it looks like Epic Games is joining the crowd of 100 person...[Read More]

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